Search results for: context based recommendation

Authors: Hoyeon Park, Yunhwan Keon, Kyoung-Jae Kim

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Location Based Social Network services (LBSNs) is a new term that combines location based service and social network service (SNS). Unlike traditional SNS, LBSNs emphasizes empirical elements in the user's actual physical location. Point-of-Interest (POI) is the most important factor to implement LBSNs recommendation system. POI information is the most popular spot in the area. In this study, we would like to recommend POI to users in a specific area through recommendation system using collaborative filtering. The process is as follows: first, we will use different data sets based on Seoul and New York to find interesting results on human behavior. Secondly, based on the location-based activity information obtained from the personalized LBSNs, we have devised a new rating that defines the user's preference for the area. Finally, we have developed an automated rating algorithm from massive raw data using distributed systems to reduce advertising costs of LBSNs.

Keywords: location-based social network services, point-of-interest, recommender systems, business analytics

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Authors: Kazuki Kamada, Haruka Yamashita

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In Japan, taxi is one of the popular transportations and taxi industry is one of the big businesses. However, in recent years, there has been a difficult problem of reducing the number of taxi drivers. In the taxi business, mainly three passenger catching methods are applied. One style is "cruising" that drivers catches passengers while driving on a road. Second is "waiting" that waits passengers near by the places with many requirements for taxies such as entrances of hospitals, train stations. The third one is "dispatching" that is allocated based on the contact from the taxi company. Above all, the cruising taxi drivers need the experience and intuition for finding passengers, and it is difficult to decide "the destination for cruising". The strong recommendation system for the cruising taxies supports the new drivers to find passengers, and it can be the solution for the decreasing the number of drivers in the taxi industry. In this research, we propose a method of recommending a destination for cruising taxi drivers. On the other hand, as a machine learning technique, the embedding models that embed the high dimensional data to a low dimensional space is widely used for the data analysis, in order to represent the relationship of the meaning between the data clearly. Taxi drivers have their favorite courses based on their experiences, and the courses are different for each driver. We assume that the course of cruising taxies has meaning such as the course for finding business man passengers (go around the business area of the city of go to main stations) and course for finding traveler passengers (go around the sightseeing places or big hotels), and extract the meaning of their destinations. We analyze the cruising history data of taxis based on the embedding model and propose the recommendation system for passengers. Finally, we demonstrate the recommendation of destinations for cruising taxi drivers based on the real-world data analysis using proposing method.

Keywords: taxi industry, decision making, recommendation system, embedding model

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Authors: Prathik Ranka, Sridhar K, Vasanth Daniel, Mithun Shankar

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With growing global food needs and climate uncertainties, informed crop choices are critical for increasing agricultural productivity. Here we propose a machine learning-based crop recommendation system to help farmers in choosing the most proper crops according to their geographical regions and soil properties. We can deploy algorithms like Decision Trees, Random Forests and Support Vector Machines on a broad dataset that consists of climatic factors, soil characteristics and historical crop yields to predict the best choice of crops. The approach includes first preprocessing the data after assessing them for missing values, unlike in previous jobs where we used all the available information and then transformed because there was no way such a model could have worked with missing data, and normalizing as throughput that will be done over a network to get best results out of our machine learning division. The model effectiveness is measured through performance metrics like accuracy, precision and recall. The resultant app provides a farmer-friendly dashboard through which farmers can enter their local conditions and receive individualized crop suggestions.

Keywords: crop recommendation, precision agriculture, crop, machine learning

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Authors: Hajer Taktak, Faouzi Moussa

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With the ubiquitous computing’s emergence and the evolution of enterprises’ needs, one of the main challenges is to build context-aware applications based on Web services. These applications have become particularly relevant in the pervasive computing domain. In this paper, we introduce our approach that optimizes the use of Web services with context notions when dealing with contextual environments. We focus particularly on making Web services autonomous and natively context-aware. We implement and evaluate the proposed approach with a pedagogical example of a context-aware Web service treating temperature values. 

Keywords: context-aware, CXF framework, ubiquitous computing, web service

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Authors: Sameh Baccari, Mahmoud Neji

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In recent years, the rapid developments on mobile devices and wireless technologies enable new dimension capabilities for the learning domain. This dimension facilitates people daily activities and shortens the distances between individuals. When these technologies have been used in learning, a new paradigm has been emerged giving birth to mobile learning. Because of the mobility feature, m-learning courses have to be adapted dynamically to the learner’s context. The main challenge in context-aware mobile learning is to develop an approach building the best learning resources according to dynamic learning situations. In this paper, we propose a context-aware mobile learning system called Collaborative and Context-aware Mobile Learning System (CCMLS). It takes into account the requirements of Mobility, Collaboration and Context-Awareness. This system is based on the semantic modeling of the learning context and the learning content. The adaptation part of this approach is made up of adaptation rules to propose and select relevant resources, learning partners and learning activities based not only on the user’s needs, but also on its current context.

Keywords: mobile learning, mobile technologies, context-awareness, collaboration, semantic web, adaptation engine, adaptation strategy, learning object, learning context

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Authors: Liwen Hou

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The booming mobile business has been penetrating the taxi industry worldwide with P2P (peer to peer) taxi services, as an emerging business model, transforming the industry. Parallel with other mobile businesses, member recommendations and online reviews are believed to be very effective with regard to acquiring new users for P2P taxi services. Based on an empirical dataset of the taxi industry in China, this study aims to reveal which factors influence users’ recommendations and review-writing behaviors. Differing from the existing literature, this paper takes the taxi driver’s perspective into consideration and hence selects a group of variables related to the drivers. We built two models to reflect the factors that influence the number of recommendations and reviews posted on the platform (i.e., the app). Our models show that all factors, except the driver’s score, significantly influence the recommendation behavior. Likewise, only one factor, passengers’ bad reviews, is insignificant in generating more drivers’ reviews. In the conclusion, we summarize the findings and limitations of the research.

Keywords: online recommendation, P2P taxi service, review-writing, word of mouth

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Authors: Fabio Fabris, Alex A. Freitas

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Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.

Keywords: algorithm recommendation, meta-learning, bioinformatics, hierarchical classification

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Authors: Smail Boussaadi, Hassina Aliane

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With the rapid growth of scientific literature, it is becoming increasingly challenging for researchers to keep up with the latest findings in their fields. Academic, professional networks play an essential role in connecting researchers and disseminating knowledge. To improve the user experience within these networks, we need effective article recommendation systems that provide personalized content.Current recommendation systems often rely on collaborative filtering or content-based techniques. However, these methods have limitations, such as the cold start problem and difficulty in capturing semantic relationships between articles. To overcome these challenges, we propose a new approach that combines BERTopic (Bidirectional Encoder Representations from Transformers), a state-of-the-art topic modeling technique, with community detection algorithms in a academic, professional network. Experiences confirm our performance expectations by showing good relevance and objectivity in the results.

Keywords: scientific articles, community detection, academic social network, recommender systems, neural topic model

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Authors: benabbess anja

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This research presents the design and implementation of a real-time course recommendation system for online learning platforms, leveraging user competencies and expertise levels. The system begins by extracting and classifying the complexity levels of courses from Udemy datasets using semantic enrichment techniques and resources such as WordNet and BERT. A predictive model assigns complexity levels to each course, adding columns that represent the course category, sub-category, and complexity level to the existing dataset. Simultaneously, user profiles are constructed through questionnaires capturing their skills, sub-skills, and proficiency levels. The recommendation process involves generating embeddings with BERT, followed by calculating cosine similarity between user profiles and courses. Courses are ranked based on their relevance, with the BERT model delivering the most accurate results. To enable real-time recommendations, Apache Kafka is integrated to track user interactions (clicks, comments, time spent, completed courses, feedback) and update user profiles. The embeddings are regenerated, and similarities with courses are recalculated to reflect users' evolving needs and behaviors, incorporating a progressive weighting of interactions for more personalized suggestions. This approach ensures dynamic and real-time course recommendations tailored to user progress and engagement, providing a more personalized and effective learning experience. This system aims to improve user engagement and optimize learning paths by offering courses that precisely match users' needs and current skill levels.

Keywords: recommendation system, online learning, real-time, user skills, expertise level, personalized recommendations, dynamic suggestions

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Authors: Ravindra Dastikop, G. S. Thyagaraju, U. P. Kulkarni

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Even though the number of context aware applications is increasing day by day along with the users, till today there is no generic programming paradigm for context aware applications. This situation could be remedied by design and developing the appropriate context modeling and programming paradigm for context aware applications. In this paper, we are proposing the static context model and metrics for validating the expressiveness and understandability of the model. The proposed context modeling is a way of describing a situation of user using context entities , attributes and relationships .The model which is an extended and hybrid version of ER model, ontology model and Graphical model is specifically meant for expressing and understanding the user situation in context aware environment. The model is useful for understanding context aware problems, preparing documentation and designing programs and databases. The model makes use of context entity attributes relationship (CEAR) diagram for representation of association between the context entities and attributes. We have identified a new set of graphical notations for improving the expressiveness and understandability of context from the end user perspective .

Keywords: user context, context entity, context entity attributes, situation, sensors, devices, relationships, actors, expressiveness, understandability

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Authors: Heba M. Wagih, Hoda M. O. Mokhtar

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Social Network has recently played a significant role in both scientific and social communities. The growing adoption of social network applications has been a relevant source of information nowadays. Due to its popularity, several research trends are emerged to service the huge volume of users including, Location-Based Social Networks (LBSN), Recommendation Systems, Sentiment Analysis Applications, and many others. LBSNs applications are among the highly demanded applications that do not focus only on analyzing the spatiotemporal positions in a given raw trajectory but also on understanding the semantics behind the dynamics of the moving object. LBSNs are possible means of predicting human mobility based on users social ties as well as their spatial preferences. LBSNs rely on the efficient representation of users’ trajectories. Hence, traditional raw trajectory information is no longer convenient. In our research, we focus on studying human behavior trajectory which is the major pillar in location recommendation systems. In this paper, we propose an ontology design patterns with their underlying description logics to efficiently annotate human behavior trajectories.

Keywords: human behavior trajectory, location-based social network, ontology, social network

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Authors: Shu-Lu Hsu, Fang-Yi Chu

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With the advances in information and communications technology, mobile context-aware applications have become powerful marketing tools. In Apple online store, there are numerous mobile applications (APPs) developed for destination tour. This study investigated the determinants of adoption of context-aware APPs for destination tour services. A model is proposed based on Technology Acceptance Model and privacy concern theory. The model was empirically tested based on a sample of 259 users of a tourism APP published by Kaohsiung Tourism Bureau, Taiwan. The results showed that the fitness of the model is well and, among all the factors, the perceived usefulness and perceived ease of use have the most significant influences on the intention to adopt context-aware destination APPs. Finally, contrary to the findings of previous literature, the effect of privacy concern on the adoption intention of context-aware APP is insignificant.

Keywords: mobile application, context-aware, privacy concern, TAM

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Authors: Sławomir Lasota, Tomasz Kajdanowicz

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This paper delves into adapting the Tweeting Factory paradigm to achieve universal production optimization under the Industry 5.0 framework. The proposed system creates a dynamic decision-making environment by collecting and analyzing structured telemetry data (”tweets”) from production lines. A hybrid recommendation engine combines rule-based systems with machine learning models to enhance real-time responsiveness and operator engagement. The research evaluates the system’s ability to optimize diverse industrial processes through predictive KPIs and real-time feedback loops. Results indicate significant advancements in eco-efficiency and operator productivity, showcasing the versatility of the Tweeting Factory approach in meeting the demands of human-centric and sustainable production.

Keywords: tweeting factory, production optimization, industry 5.0, recommendation

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Authors: Mostafa Gamal, Hoda K. Mohamed, Islam El-Maddah, Ali Hamdi

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In recent years, many digital documents on the web have been created due to the rapid growth of ’social applications’ communities or ’Data-intensive applications’. The evolution of online-based multimedia data poses new challenges in storing and querying large amounts of data for online recommender systems. Graph data models have been shown to be more efficient than relational data models for processing complex data. This paper will explain the key differences between graph and relational databases, their strengths and weaknesses, and why using graph databases is the best technology for building a realtime recommendation system. Also, The paper will discuss several similarity metrics algorithms that can be used to compute a similarity score of pairs of nodes based on their neighbourhoods or their properties. Finally, the paper will discover how NLP strategies offer the premise to improve the accuracy and coverage of realtime recommendations by extracting the information from the stored unstructured knowledge, which makes up the bulk of the world’s data to enrich the graph database with this information. As the size and number of data items are increasing rapidly, the proposed system should meet current and future needs.

Keywords: graph databases, NLP, recommendation systems, similarity metrics

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Authors: Anna Czarczynska

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Creativity is taken as a requirement for a personal anti-fragile career path in the context of regional competitive advantage in the terms of socio-economics creative environment. At the personal level, the competence and value-based approach to creativity are proposed, is an elaboration of the resource-based view of the group of individuals selected from given country. Entrepreneurial creativity competence (measured by the Schein anchor questionnaire) is based on an independent way of thinking and empowerment presents one aspect of creative capability, however quickly verified by the market, that’s why we treat this as a basic exemplification of average creative attitude combine with the entrepreneurial attitude. This introductory instrument enables further scientific research based on the same group in the context of multi-cultural external creative or the non-creative environment.

Keywords: creativity, value-based approach, entrepreneurship, regional culture

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Authors: Xochilt Ramirez-Garcia, Mario Garcia-Valdez

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Contextual recommendations are implemented in Recommender Systems to improve user satisfaction, recommender system makes accurate and suitable recommendations for a particular situation reaching personalized recommendations. The context provides information relevant to the Recommender System and is used as a filter for selection of relevant items for the user. This paper presents a Context-aware Recommender System, which uses techniques based on Collaborative Filtering and Content-Based, as well as fuzzy rules, to recommend items inside the context. The dataset used to test the system is Trip Advisor. The accuracy in the recommendations was evaluated with the Mean Absolute Error.

Keywords: algorithms, collaborative filtering, intelligent systems, fuzzy logic, recommender systems

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Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Jesse D. Cano, Marlon J. Remedios

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Cuban tourism has developed so much in the last 30 years to the point of becoming one of the engines of the Cuban economy. With such a development, Cuban companies opting for e-tourism as a way to publicize their products and attract customers has also grown. Despite this fact, the majority of Cuban tourism-themed websites simply provide information on the different products and services they offer which results in many cases, in the user getting overwhelmed with the amount of information available which results in the user abandoning the search before he can find a product that fits his needs. Customization has been recognized as a critical factor for successful electronic tourism business and the use of recommender systems is the best approach to address the problem of personalization. This paper aims to outline a preliminary technique to obtain predictions about which products a particular user would give a better evaluation; these products would be those which the website would show in the first place. To achieve this, the theoretical elements of the Cuban tourism environment are discussed; recommendation systems and geographic information systems as tools for information representation are also discussed. Finally, for each structural component identified, we define a set of rules that allows obtaining an electronic tourism system that handles the personalization of the service provided effectively.

Keywords: geographic information system, technique, tourism products, recommendation

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Authors: Patik Joslin Kenfack, Polyakov Vladimir Mikhailovich

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Machine Learning (ML) systems are trained using human generated data that could be biased by implicitly containing racist, sexist, or discriminating data. ML models learn those biases or even amplify them. Recent research in work on has begun to consider issues of fairness. The concept of fairness is extended to recommendation. A recommender system will be considered fair if it doesn’t under rank items of protected group (gender, race, demographic...). Several metrics for evaluating fairness concerns in recommendation systems have been proposed, which take pairs of items as ‘instances’ in fairness evaluation. It doesn’t take in account the fact that the fairness should be evaluated across a list of items. The paper explores a probabilistic approach that generalize pairwise metric by using a list k (listwise) of items as ‘instances’ in fairness evaluation, parametrized by k. We also explore new regularization method based on this metric to improve fairness ranking during model training.

Keywords: Fairness, Recommender System, Ranking, Listwise Approach

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Authors: Zhaohan Xie, Yining Yu, Mingliang Chen

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As research has shown a convergent tendency for aversion to AI recommendation, it is imperative to find a way to promote AI usage and better harness the technology. In the context of e-commerce, this study has found evidence that people show less avoidance of algorithms when recommending search products compared to experience products. This is due to people’s different attribution of mind to AI versus humans, as suggested by mind perception theory. While people hold the belief that an algorithm owns sufficient capability to think and calculate, which makes it competent to evaluate search product attributes that can be obtained before actual use, they doubt its capability to sense and feel, which is essential for evaluating experience product attributes that must be assessed after experience in person. The result of the behavioral investigation (Study 1, N=112) validated that consumers show low purchase intention to experience products recommended by AI. Further consumer neuroscience study (Study 2, N=26) using Event-related potential (ERP) showed that consumers have a higher level of cognitive conflict when faced with AI recommended experience product as reflected by larger N2 component, while the effect disappears for search product. This research has implications for the effective employment of AI recommenders, and it extends the literature on e-commerce and marketing communication.

Keywords: algorithm recommendation, consumer behavior, e-commerce, event-related potential, experience product, search product

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Authors: Ma Cecilia Siva

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This study introduces a Bidirectional Encoder Representations from Transformers (BERT)-based machine learning model aimed at improving educational counseling by automating the process of recommending academic strands for students. The framework is designed to streamline and enhance the strand selection process by analyzing students' profiles and suggesting suitable academic paths based on their interests, strengths, and goals. Data was gathered from a sample of 200 grade 10 students, which included personal essays and survey responses relevant to strand alignment. After thorough preprocessing, the text data was tokenized, label-encoded, and input into a fine-tuned BERT model set up for multi-label classification. The model was optimized for balanced accuracy and computational efficiency, featuring a multi-category classification layer with sigmoid activation for independent strand predictions. Performance metrics showed an F1 score of 88%, indicating a well-balanced model with precision at 80% and recall at 100%, demonstrating its effectiveness in providing reliable recommendations while reducing irrelevant strand suggestions. To facilitate practical use, the final deployment phase created a recommendation framework that processes new student data through the trained model and generates personalized academic strand suggestions. This automated recommendation system presents a scalable solution for academic guidance, potentially enhancing student satisfaction and alignment with educational objectives. The study's findings indicate that expanding the data set, integrating additional features, and refining the model iteratively could improve the framework's accuracy and broaden its applicability in various educational contexts.

Keywords: tokenized, sigmoid activation, transformer, multi category classification

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Authors: Nousheen Tariq Bhutta, Syed Zulfiqar Ali Shah

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Financial crises become a key impediment towards the development of countries especially in emerging economies. Based on standard finance, many researchers investigated the financial crises in different countries in order to find the underlying reason regarding occurrence these event; however they were unable to provide it. In this essence behavioral finance may be helpful in providing answers to some queries regarding occurrence and prevention of financial crises. In this paper, we explore the some psychological factors comprises of our inspiration, emotion, cognition and culture along with their reflection companies, financial markets and governments that present some supportive arguments. Moreover, we compared the views of Keynes and Minsky in order to validate the underling justification towards occurrence of financial crises and their prevention in future. This study helps the practitioners and policy makers through providing valuable recommendation in order to protect the economies.

Keywords: financial crises, behavioral finance, financial markets, emerging economies

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Authors: Ritesh Kumar, Rajendra Pamula

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In this paper, we present our contribution for book recommendation. In our experiment, we combine the results of Sequential Dependence Model (SDM) and exploitation of book information such as reviews, tags and ratings. This social information is assigned by users. For this, we used CLEF-2016 Social Book Search Track Suggestion task. Finally, our proposed method extensively evaluated on CLEF -2015 Social Book Search datasets, and has better performance (nDCG@10) compared to other state-of-the-art systems. Recently we got the good performance in CLEF-2016.

Keywords: sequential dependence model, social information, social book search, query expansion

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Authors: Abul Hassan

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Present socio-economic context and women wellbeing in Bangladesh imposes lots of constraints on women’s involvement in income generating activities. Different studies showed that the implementation of World Bank structural adjustment policies have had mixed impacts on women and their wellbeing. By involving poor people specially women in Islamic microfinance programmes in Bangladesh are used as a tool to combat poverty. Women are specifically targeted by Islamic microfinance under the rural development scheme of Islami Bank Bangladesh that provide interest free loan to the women groups. The programme has a multiplier effect since women invest largely in their households. The aim of this research is twofold: firstly, it wanted to confirm or refute a positive link between Islamic microfinance and the socio-economic wellbeing of women in Bangladesh and secondly, to explore the context in which Islamic microfinance programs function in Bangladesh and the way their performance can be improved. Based on structured questionnaires’ survey, this study addressed two research questions: (1) What can be expected from the offer of Islamic microfinance on the welfare of recipients and (2) Under what conditions would such an offer be more beneficial. The main result of this study shows that increase in women’s income and assets played a very important role in enhancing women’s economic independence and sense of self-confidence. An important policy recommendation is that it is necessary to redirect Islamic microfinance towards diversified developmental activities that will contribute to the improvement, in the long run, of the wellbeing of the recipients.

Keywords: business model, Islamic microfinance, women’s wellbeing

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Authors: C. L. Huang, J. J. Sia

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The interest-based social networks, also known as social bookmark sharing systems, are useful platforms for people to conveniently read and collect internet resources. These platforms also providing function of social networks, and users can share and explore internet resources from the social networks. Providing personalized internet resources to users is an important issue on these platforms. This study uses two types of relationship on the social networks—following and follower and proposes a collaborative recommender system, consisting of two main steps. First, this study calculates the relationship strength between the target user and the target user's followings and followers to find top-N similar neighbors. Second, from the top-N similar neighbors, the articles (internet resources) that may interest the target user are recommended to the target user. In this system, users can efficiently obtain recent, related and diverse internet resources (knowledge) from the interest-based social network. This study collected the experimental dataset from Diigo, which is a famous bookmark sharing system. The experimental results show that the proposed recommendation model is more accurate than two traditional baseline recommendation models but slightly lower than the cosine model in accuracy. However, in the metrics of the diversity and executing time, our proposed model outperforms the cosine model.

Keywords: recommender systems, social networks, tagging, bookmark sharing systems, collaborative recommender systems, knowledge management

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Authors: Xiaohui Zhao, Shahan Mafuz

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Context-aware technologies provide system applications with the awareness of environmental conditions, customer behaviour, object movements, etc. Further, with such capability system applications can be smart to adapt intelligently their responses to the changing conditions. Concerning business operations, this promises businesses that their business processes can run more intelligently, adaptively and flexibly, and thereby either improve customer experience, enhance reliability of service delivery, or lower operational cost, to make the business more competitive and sustainable. Aiming at realizing such context-aware business process management, this paper firstly explores its potential benefit and then identifies some gaps between the current business process management support and the expected. In addition, some preliminary solutions are also discussed with context definition, rule-based process execution, run-time process evolution, etc. A framework is also presented to give a conceptual architecture of context-aware business process management system to guide system implementation.

Keywords: business process adaptation, business process evolution, business process modelling, and context awareness

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Authors: Peter M. Maurer Maurer

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DGL is a context free grammar-based tool for generating random data. Many types of simulator input data require some computation to be placed in the proper format. For example, it might be necessary to generate ordered triples in which the third element is the sum of the first two elements, or it might be necessary to generate random numbers in some sorted order. Although DGL is universal in computational power, generating these types of data is extremely difficult. To overcome this problem, we have enhanced DGL to include features that permit direct computation within the structure of a context free grammar. The features have been implemented as special types of productions, preserving the context free flavor of DGL specifications.

Keywords: DGL, Enhanced Context Free Grammars, Programming Constructs, Random Data Generation

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Authors: Farideh Doostan, Sahar Mohseni Taklloo, Mohammad Nosrati

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Chronic diseases are non-communicable and largely preventable by lifestyle changes including healthy diet consumption. They are the most common cause of death in the world and projected to increase by 15% globally between 2010 and 2020.The hazardous effects of behavioral and dietary risk factors on chronic disease have been established in prospective cohort studies and randomized trials. Because of some changes occur in college students’ lifestyle, assessment of dietary risk factors is important in these populations. Objective: This research was the first study that conducted to evaluate dietary intakes of Kermanian college students with WHO/FAO nutritional objectives. Material and Methods: In this descriptive cross-sectional study, 229 healthy college students of health faculty in Kerman University of Medical Sciences that do not intake any medical drugs were recruited using multistage sampling in 2013.Usual dietary intake was collected using a valid Food Frequency Questionnaire (FFQ) and diet quality was calculated based on WHO nutrient goals. To analysis of data between two groups, independent sample t. test and man whitney were applied. Results: Two hundred and twenty-nine college students; 151 females (65.9%) and 78 males (34.1%), the mean age of 21.9 years were studied. The mean of the Body Mass Index (Kg/m2) and Waist Circumference (cm) in males were 22.34 ±3.52 and 80.76±11.16 and in females were 21.19±2.62 and 73.67±7.65 respectively. Mean of daily cholesterol intake in males was significantly more than females (305±101 VS 268±98; P=0.008) and more than WHO/FAO recommendation (less than 300 mg/day). The mean of daily sodium intake in men and women were 10.4±1 and 10.9±5.3 respectively. These amounts were more than WHO/FAO recommendation (less than 2g/day). In addition, women were consumed fruit and vegetables more than men (839±336 VS 638±281; p ‹ 0.001) and these amounts were more than WHO/FAO recommendation (more than 400g/day) in both groups. Other intake indices were in the range of WHO/FAO recommendations, So that Percent of calories intake from total fat, saturated fatty acids, polyunsaturated fatty acids and added sugar were in compliance with WHO/FAO recommendations. Conclusion: Cholesterol intake in men and sodium intake in all participants were more than WHO/FAO recommendation. These dietary components are the most important causes of cardiovascular disease (one of the main causes of death in our population). These results indicated that proper nutritional education and interventions are needed in this population.

Keywords: college students, food intake, WHO /FAO nutrient intake goals, Kerman

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Authors: Matthew T. Wilson

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This paper covers a selection of research utilizing grammar-based genetic programming, and illustrates how context-free grammar can be used to constrain genetic programming. It focuses heavily on grammatical evolution, one of the most popular variants of grammar-based genetic programming, and the way its operators and terminals are specialized and modified from those in genetic programming. A variety of implementations of grammatical evolution for general use are covered, as well as research each focused on using grammatical evolution or grammar-based genetic programming on a single application, or to solve a specific problem, including some of the classically considered genetic programming problems, such as the Santa Fe Trail.

Keywords: context-free grammar, genetic algorithms, genetic programming, grammatical evolution

Procedia PDF Downloads 188