Ramon Lopes
ABSTRACT
Short-length random walks on the bipartite user-item graph have recently been shown to provide accurate and diverse recommendations. Nonetheless, these approaches suffer from severe time and space requirements, which can be alleviated via random walk sampling, at the cost of reduced recommendation quality. In addition, these approaches ignore users' ratings, which further limits their expressiveness. In this paper, we introduce a computationally efficient graph-based approach for collaborative filtering based on short-path enumeration. Moreover, we propose three scoring functions based on the Bayesian paradigm that effectively exploit distributional aspects of the users' ratings. We experiment with seven publicly available datasets against state-of-the-art graph-based and matrix factorization approaches. Our empirical results demonstrate the effectiveness of the proposed approach, with significant improvements in most settings. Furthermore, analytical results demonstrate its efficiency compared to other graph-based approaches.
Supplemental Material
- F. Christoffel, B. Paudel, C. Newell, and A. Bernstein. Blockbusters and wallflowers: Accurate, diverse, and scalable recommendations with random walks. In Proceedings of the 9th ACM Conference on Recommender Systems, pages 163--170, New York, NY, USA, 2015. ACM. Google Scholar
Digital Library
- J. Cook. Exact calculation of beta inequalities. Technical report, Department of Biostatistics, November 2005.Google Scholar
- C. Cooper, S. H. Lee, T. Radzik, and Y. Siantos. Random walks in recommender systems: Exact computation and simulations. In Proceedings of the 23rd International Conference on World Wide Web, pages 811--816, New York, NY, USA, 2014. ACM. Google ScholarDigital Library
- F. Fouss, A. Pirotte, and M. Saerens. A novel way of computing similarities between nodes of a graph, with application to collaborative recommendation. In Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pages 550--556, Washington, DC, USA, 2005. IEEE Computer Society. Google ScholarDigital Library
- F. Fouss, A. Pirotte, J.-m. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 19 (3): 355--369, 2007. Google ScholarDigital Library
- M. Galassi et al. GNU Scientific Library Reference Manual. Network Theory Ltd., 3rd edition, 2009. Google ScholarDigital Library
- Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Mymedialite: A free recommender system library. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys '11, pages 305--308, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- J. Golbeck and J. Hendler. Filmtrust: movie recommendations using trust in web-based social networks. In Proceedings of Consumer the 3rd IEEE Communications and Networking Conference, pages 282--286. IEEE, 2006.Google ScholarCross Ref
- M. Gori and A. Pucci. Itemrank: A random-walk based scoring algorithm for recommender engines. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, pages 2766--2771, San Francisco, CA, USA, 2007. Morgan Kaufmann Publishers Inc. Google ScholarDigital Library
- F. M. Harper and J. A. Konstan. The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems, 5 (4): 19:1--19:19, Dec. 2015. Google ScholarDigital Library
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM '08, pages 263--272, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarDigital Library
- elin and Kek\"al\"ainen(2002)}jarvelin:2002K. J\"arvelin and J. Kekalainen. Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems, 20 (4): 422--446, Oct. 2002. Google ScholarDigital Library
- P. Massa and P. Avesani. Trust-aware recommender systems. In Proceedings of the 2007 ACM Conference on Recommender Systems, RecSys '07, pages 17--24, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- J. McAuley, R. Pandey, and J. Leskovec. Inferring networks of substitutable and complementary products. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785--794. ACM, 2015. Google ScholarDigital Library
- L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical Report 1999--66, Stanford InfoLab, November 1999.Google Scholar
- Y. Park and A. Tuzhilin. The long tail of recommender systems and how to leverage it. In Proceedings of the 2008 ACM Conference on Recommender Systems, pages 11--18, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI '09, pages 452--461, Arlington, Virginia, United States, 2009. AUAI Press. Google ScholarDigital Library
- G. Shani and A. Gunawardana. Recommender Systems Handbook, chapter Evaluating Recommendation Systems, pages 257--297. Springer US, Boston, MA, 2011.Google Scholar
- Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic. Climf: Learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys '12, pages 139--146, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- A. P. Singh, A. Gunawardana, C. Meek, and A. C. Sudendran. Recommendations using absorbing random walks. In North East Student Colloquium on Artificial Intelligence, 2007.Google Scholar
- C. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web, pages 22--32, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
Index Terms
- Efficient Bayesian Methods for Graph-based Recommendation
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