Abstract
Neighborhood-based approaches often fail in sparse scenarios; a direct implication for recommender systems exploiting co-occurring items is often an inappropriately poor performance. As a remedy, we propose to propagate information (e.g., similarities) across the item graph to leverage sparse data. Instead of processing only directly connected items (e.g. co-occurrences), the similarity of two items is defined as the maximum capacity path interconnecting them. Our approach resembles a generalization of neighborhood-based methods that are obtained as special cases when restricting path lengths to one. We present two efficient online computation schemes and report on empirical results.
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Notes
- 1.
Note that paths are usually cycle free by definition and capacities do not change by repeating cycles.
References
Aiolli, F.: Efficient top-n recommendation for very large scale binary rated datasets. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 273–280. ACM (2013)
De Baets, B., De Meyer, H.: On the existence and construction of t-transitive closures. Inf. Sci. 152, 167–179 (2003)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische mathematik 1(1), 269–271 (1959)
Gantner, Z., Drumond, L., Freudenthaler, C., Rendle, S., Schmidt-Thieme, L.: Learning attribute-to-feature mappings for cold-start recommendations. In: Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM 2010, pp. 176–185. IEEE Computer Society, Washington, DC (2010)
Gemulla, R., Nijkamp, E., Haas, P.J., Sismanis, Y.: Large-scale matrix factorization with distributed stochastic gradient descent. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2011)
Gori, M., Pucci, A., Roma, V., Siena, I.: ItemRank: a random-walk based scoring algorithm for recommender engines. IJCAI 7, 2766–2771 (2007)
Hansen, P.: Bicriterion path problems. In: Fandel, G., Gal, T. (eds.) Multiple Criteria Decision Making Theory and Application: Proceedings of the Third Conference, pp. 109–127. Springer, Heidelberg (1980). doi:10.1007/978-3-642-48782-8_9
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TiiS) 5(4), 19 (2016)
Henig, M.I.: The shortest path problem with two objective functions. Eur. J. Oper. Res. 25(2), 281–291 (1986)
Hofmann, T.: Probabilistic latent semantic analysis. In: Proceedings of Uncertainty in Artificial Intelligence (1999)
Hu, T.C.: Letter to the editor-the maximum capacity route problem. Oper. Res. 9(6), 898–900 (1961)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining (2008)
Kang, U., Bilenko, M., Zhou, D., Faloutsos, C.: Axiomatic analysis of co-occurrence similarity functions. Technical report CMU-CS-12-102, School of Computer Science, Carnegie Mellon University (2012)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM (2008)
Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Disc. Data (TKDD) 4(1), 1 (2010)
Li, M., Dias, B., El-Deredy, W., Lisboa, P.J.G.: A probabilistic model for item-based recommender systems. In: Proceedings of the ACM Conference on Recommender Systems (2007)
Malucelli, F., Cremonesi, P., Rostami, B.: An application of bicriterion shortest paths to collaborative filtering. In: Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS) (2012)
Pollack, M.: Letter to the editor-the maximum capacity through a network. Oper. Res. 8(5), 733–736 (1960)
Popescul, A., Ungar, L., Pennock, D.M., Lawrence, S.: Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: Proceedings of the Conference on Uncertainity in Artificial Intelligence (2001)
Raith, A., Ehrgott, M.: A comparison of solution strategies for biobjective shortest path problems. Comput. Oper. Res. 36(4), 1299–1331 (2009)
Rendle, S., Schmidt-Thieme, L.: Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In: Proceedings of the ACM Conference on Recommender Systems (2008)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender system-a case study. Technical report, DTIC Document (2000)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the International World Wide Web Conference (2010)
Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2002)
Simas, T., Rocha, L.M.: Semi-metric networks for recommender systems. In: Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, vol. 03, pp. 175–179. IEEE Computer Society (2012)
Skriver, A.J., Andersen, K.A.: A label correcting approach for solving bicriterion shortest-path problems. Comput. Oper. Res. 27(6), 507–524 (2000)
Wang, J., Sarwar, B., Sundaresan, N.: Utilizing related products for post-purchase recommendation in e-commerce. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 329–332. ACM (2011)
Wartena, C., Brussee, R., Wibbels, M.: Using tag co-occurrence for recommendation. In: Proceedings of the International Conference on Intelligent Systems Design and Applications (2009)
Acknowledgments
This research has been funded in parts by the German Federal Ministry of Education and Science BMBF under grant QQM/01LSA1503C and the Brazilian CAPES Foundations and a grant from CNPq.
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Boubekki, A., Brefeld, U., Lucchesi, C.L., Stille, W. (2017). Propagating Maximum Capacities for Recommendation. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_6
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