Sistemas Recomendadores - IIC 3633

Clases Semana 1

• Introducción
• Ranking no personalizado + Filtrado Colaborativo (User-Based) slides pdf
• Filtrado Colaborativo (User-Based) con Clustering slides pdf

Lecturas Semana 1

Obligatorias

• How not to sort by Average Rating, Evan Miller Blog
• Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web (pp. 291-324). Springer Berlin Heidelberg.

Sugeridas

• Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994, October). GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work (pp. 175-186). ACM.
• Shardanand, Upendra, and Pattie Maes. “Social information filtering: algorithms for automating “word of mouth”." Proceedings of the SIGCHI conference on Human factors in computing systems. ACM Press/Addison-Wesley Publishing Co., 1995.
• Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70.
• How hacker news ranking algorithm works, link
• How reedit ranking algorithm works, link
• Reddit comment ranking algorithm, link

Clases Semana 2

• Item-Based Collaborative Filtering slides pdf
• Slope 1 slides pdf
• Factorización Matricial slides pdf
• Retroalimentación Implícita (Implicit Feedback) slides pdf

Lecturas Semana 2

Obligatorias

• Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer IEEE Magazine, 42(8), 30-37.
• Hu, Y., Koren, Y., & Volinsky, C. (2008, December). Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM’08. Eighth IEEE International Conference on (pp. 263-272). IEEE.

Sugeridas

• Lemire, D., & Maclachlan, A. (2005). Slope One Predictors for Online Rating-Based Collaborative Filtering. In SDM (Vol. 5, pp. 1-5).
• Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999, August). An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 230-237).
• Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1), 76-80.
• Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web (pp. 291-324). Springer Berlin Heidelberg.
• Wang, J., De Vries, A. P., & Reinders, M. J. (2006, August). Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 501-508). ACM.
• Takács, G., Pilászy, I., Németh, B., & Tikk, D. (2009). Scalable collaborative filtering approaches for large recommender systems. Journal of machine learning research, 10(Mar), 623-656.
• Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295). ACM.
• Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Application of dimensionality reduction in recommender system-a case study (No. TR-00-043). Minnesota Univ Minneapolis Dept of Computer Science.
• Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2002). Incremental singular value decomposition algorithms for highly scalable recommender systems. In Fifth International Conference on Computer and Information Science (pp. 27-28)

Lecturas Semana 3

Obligatorias

• Shani, Guy, and Asela Gunawardana. “Evaluating recommendation systems.” In Recommender systems handbook, pp. 257-297. Springer US, 2011.

Sugeridas Evaluacion

• Parra, D., & Sahebi, S. (2013). Recommender systems: Sources of knowledge and evaluation metrics. In Advanced Techniques in Web Intelligence-2 (pp. 149-175). Springer Berlin Heidelberg. pre-print pdf
• Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5-53.
• Shani, Guy, and Asela Gunawardana. “Evaluating recommendation systems.” In Recommender systems handbook, pp. 257-297. Springer US, 2011.
• The 10 recommender system metrics you should know about, GraphLab Blog
• Alan Said and Alejandro Bellogín. 2014. Comparative recommender system evaluation: benchmarking recommendation frameworks. In Proceedings of the 8th ACM Conference on Recommender systems (RecSys ’14).

Sugeridas Retroalimentación Implícita

• Oard, D. W., & Kim, J. (1998, July). Implicit feedback for recommender systems. In Proceedings of the AAAI workshop on recommender systems (pp. 81-83).
• Baltrunas, L., & Amatriain, X. (2009, October). Towards time-dependant recommendation based on implicit feedback. In Workshop on context-aware recommender systems (CARS’09).
• Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009, June). BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (pp. 452-461). AUAI Press.
• Parra, D., Karatzoglou, A., Amatriain, X., & Yavuz, I. (2011). Implicit feedback recommendation via implicit-to-explicit ordinal logistic regression mapping. Proceedings of the CARS-2011.
• Hidasi, B., & Tikk, D. (2012). Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback. In Machine Learning and Knowledge Discovery in Databases (pp. 67-82). Springer Berlin Heidelberg.

Clases Semana 4

• Recomendación Basada en Contenido II slides pdf
• Practico Recomendacion Basada en Contenido instrucciones en pdf
• Recomendación Híbrida slides pdf (fecha blog post: 20 septiembre)

Lecturas Semana 4

Obligatorias

• No hay lecturas obligatorias esta semana

Sugeridas

• Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The adaptive web (pp. 325-341). Springer Berlin Heidelberg.
• Robin Burke. 2002. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12, 4 (November 2002), 331-370. DOI=10.1023/A:1021240730564 http://dx.doi.org/10.1023/A:1021240730564
• Balabanović, M., & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72.
• Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook (pp. 73-105). Springer US.
• De Gemmis, M., Lops, P., Semeraro, G., & Basile, P. (2008, October). Integrating tags in a semantic content-based recommender. In Proceedings of the 2008 ACM conference on Recommender systems (pp. 163-170). ACM.
• Parra, D., & Brusilovsky, P. (2009, October). Collaborative filtering for social tagging systems: an experiment with CiteULike. In Proceedings of the third ACM conference on Recommender systems (pp. 237-240). ACM.

Clases Semana 5

• Recomendación Context-Aware slides pdf
  • Presentación de profesor R. Santos en LSRS 2017 Workshop pdf
  • Presentación de artículo “Understanding the Impact of Weather for POI Recommendation” pdf
  • También se recomienda revisar las slides de RecSys 2015 Best Paper

Lecturas Semana 5

Obligatorias

• Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The adaptive web (pp. 325-341). Springer Berlin Heidelberg.

Sugeridas

• http://www.aaai.org/ojs/index.php/aimagazine/article/view/2364
• Karatzoglou, A., Amatriain, X., Baltrunas, L., & Oliver, N. (2010, September). Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems (pp. 79-86). ACM.
• Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A., & Oliver, N. (2012, August). TFMAP: Optimizing MAP for top-n context-aware recommendation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (pp. 155-164). ACM.
• Augusto Q. Macedo, Leandro B. Marinho, and Rodrygo L.T. Santos. 2015. Context-Aware Event Recommendation in Event-based Social Networks. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15). ACM, New York, NY, USA, 123-130. DOI=http://dx.doi.org/10.1145/2792838.2800187

Lecturas Semana 6

Obligatorias Rendle, S. (2010). Factorization machines. In Data Mining (ICDM), 2010 IEEE 10th International Conference on (pp. 995-1000). IEEE.

Sugeridas - Rendle, S. (2012). Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3), 57.