Hybrid Collaborative-Context Based Recommendations for Civil Affairs Operations

Authors: Patrick Cummings, Laura Cassani, Deirdre Kelliher

Abstract:

In this paper we present findings from a research effort to apply a hybrid collaborative-context approach for a system focused on Marine Corps civil affairs data collection, aggregation, and analysis called the Marine Civil Information Management System (MARCIMS). The goal of this effort is to provide operators with information to make sense of the interconnectedness of entities and relationships in their area of operation and discover existing data to support civil military operations. Our approach to build a recommendation engine was designed to overcome several technical challenges, including 1) ensuring models were robust to the relatively small amount of data collected by the Marine Corps civil affairs community; 2) finding methods to recommend novel data for which there are no interactions captured; and 3) overcoming confirmation bias by ensuring content was recommended that was relevant for the mission despite being obscure or less well known. We solve this by implementing a combination of collective matrix factorization (CMF) and graph-based random walks to provide recommendations to civil military operations users. We also present a method to resolve the challenge of computation complexity inherent from highly connected nodes through a precomputed process.

Keywords: Recommendation engine, collaborative filtering, context based recommendation, graph analysis, coverage, civil affairs operations, Marine Corps.

References:

[1] Y. Zheng, B. Tang, W. Ding, and H. Zhou. 2016. A Neural Autoregressive Approach to Collaborative Filtering. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (ICML’16). JMLR.org, 764–773. http://dl.acm.org/citation.cfm?id=3045390.3045472
[2] Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan. 2008. Large-Scale Parallel Collaborative Filtering for the Netflix Prize. In Proceedings of the 4th International Conference on Algorithmic Aspects in Information and Management (AAIM ’08). Springer-Verlag, Berlin, Heidelberg, 337–348. https://doi.org/10.1007/978-3-540-68880-8_32
[3] F. Ricci, L. Rokach, and B. Shapira, Introduction to Recommender Systems Handbook, Recommender Systems Handbook, Springer, 2011, pp. 1-35
[4] M. Liu, X. Xie, and H. Zhou. 2018. Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline. CoRR abs/1806.00737 (2018). arXiv:1806.00737 http://arxiv.org/abs/1806.00737
[5] J. Wilson, S. Chaudhury, and B. Lall. 2014. Improving Collaborative Filtering Based Recommenders Using Topic Modelling. In Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 01 (WI-IAT ’14). IEEE Computer Society, Washington, DC, USA, 340–346. https://doi.org/10.1109/WI-IAT.2014.54
[6] K. Garimella, G. D. F. Morales, A. Gionis, and M. Mathioudakis. 2017. Reducing Controversy by Connecting Opposing Views. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM ’17). ACM, New York, NY, USA, 81–90. DOI:http://dx.doi.org/10.1145/3018661.3018703
[7] R. V. Meteren and M. V. Someren. Using content-based filtering for recommendation. In Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, pages 47–56, 2000.
[8] K. Verbert, N. Manouselis, X. Ochoa, M. Wolpers, H. Drachsler, I. Bosnic, and E. Duval. Context-aware recommender systems for learning: a survey and future challenges. IEEE Transactions on Learning Technologies, 5(4):318–335, 2012.
[9] A. P. Singh and G. J. Gordon. 2008. Relational Learning via Collective Matrix Factorization. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’08). ACM, New York, NY, USA, 650–658. https://doi.org/10.1145/1401890.1401969
[10] M. Kula. 2015. Metadata Embeddings for User and Item Cold-start Recommendations. In Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems co-located with 9th ACM Conference on Recommender Systems (RecSys 2015), Vienna, Austria, September 16-20, 2015. (CEUR Workshop Proceedings), Toine Bogers and Marijn Koolen (Eds.), Vol. 1448. CEUR-WS.org, 14–21. http://ceur-ws.org/Vol-1448/paper4.pdf
[11] M. Honniba and I. Montani. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. 2017.
[12] G. Adomavicius and Y. Kwon, "Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques," in IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 5, pp. 896-911, May 2012, doi: 10.1109/TKDE.2011.15.
[13] M. Kaminskas and D. Bridge, “Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems,”ACM Trans. Interact. Intell. Syst., vol. 7, pp. 2:1–2:42,Dec. 2016.
[14] M. Ge, C. Delgado-Battenfeld, and D. Jannach, “Beyond accuracy: Evaluating recommender systems by coverage and serendipity,” in Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys ’10, (NewYork, NY, USA), pp. 257–260, ACM, 2010.