Asmaa Elbadrawy
Abstract
Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Noncollaborative user modeling techniques that rely on the item features can be used to recommend new items. However, they only use the past preferences of each user to provide recommendations for that user. They do not utilize information from the past preferences of other users, which can potentially be ignoring useful information. More recent factor models transfer knowledge across users using their preference information in order to provide more accurate recommendations. These methods learn a low-rank approximation for the preference matrix, which can lead to loss of information. Moreover, they might not be able to learn useful patterns given very sparse datasets. In this work, we present <scp>UFSM</scp>, a method for top-<i>n</i> recommendation of new items given binary user preferences. <scp>UFSM</scp> learns <b>U</b>ser-specific <b>F</b>eature-based item-<b>S</b>imilarity <b>M</b>odels, and its strength lies in combining two points: (1) exploiting preference information across all users to learn multiple global item similarity functions and (2) learning user-specific weights that determine the contribution of each global similarity function in generating recommendations for each user. <scp>UFSM</scp> can be considered as a sparse high-dimensional factor model where the previous preferences of each user are incorporated within his or her latent representation. This way, <scp>UFSM</scp> combines the merits of item similarity models that capture local relations among items and factor models that learn global preference patterns. A comprehensive set of experiments was conduced to compare <scp>UFSM</scp> against state-of-the-art collaborative factor models and noncollaborative user modeling techniques. Results show that <scp>UFSM</scp> outperforms other techniques in terms of recommendation quality. <scp>UFSM</scp> manages to yield better recommendations even with very sparse datasets. Results also show that <scp>UFSM</scp> can efficiently handle high-dimensional as well as low-dimensional item feature spaces.
- Deepak Agarwal and Bee-Chung Chen. 2009. Regression-based latent factor models. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09). ACM, 19--28. Google Scholar
Digital Library
- Amr Ahmed, Bhargav Kanagal, Sandeep Pandey, Vanja Josifovski, Lluis Garcia Pueyo, and Jeffrey Yuan. 2013. Latent factor models with additive and hierarchically-smoothed user preferences. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM’13). ACM, 385--394. Google ScholarDigital Library
- Evangelos Banos, Ioannis Katakis, Nick Bassiliades, Grigorios Tsoumakas, and Ioannis P. Vlahavas. 2006. PersoNews: A personalized news reader enhanced by machine learning and semantic filtering. In Proceedings of the 2006 Confederated International Conference on the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I (ODBASE’06/OTM’06). Springer-Verlag, Berlin, 975--982. DOI:http://dx.doi.org/10.1007/11914853_62 Google ScholarDigital Library
- Daniel Billsus and Michael J. Pazzani. 1999. A hybrid user model for news story classification. In Proceedings of the 7th International Conference on User Modeling. 99--108. Google ScholarDigital Library
- Léon Bottou. 1998. Online algorithms and stochastic approximations. In Online Learning and Neural Networks, David Saad (Ed.). Cambridge University Press, Cambridge, UK. http://leon.bottou.org/papers/bottou-98x. Google ScholarDigital Library
- Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2011. 2nd workshop on information heterogeneity and fusion in recommender systems (HetRec 2011). In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11). Google ScholarDigital Library
- Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, and Schmidt-Thie Lars. 2010. Learning attribute-to-feature mappings for cold-start recommendations. In Proceedings of the 2010 IEEE International Conference on Data Mining (ICDM’10). IEEE Computer Society, Washington, DC, 176--185. Google ScholarDigital Library
- Rong Hu and Pearl Pu. 2011. Enhancing collaborative filtering systems with personality information. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11). ACM, New York, NY, 197--204. DOI:http://dx.doi.org/10.1145/2043932.2043969 Google ScholarDigital Library
- Santosh Kabbur, Xia Ning, and George Karypis. 2013. FISM: Factored item similarity models for top-N recommender systems. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13). ACM, 659--667. Google ScholarDigital Library
- Gilad Katz, Nir Ofek, Bracha Shapira, Lior Rokach, and Guy Shani. 2011. Using Wikipedia to boost collaborative filtering techniques. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11). ACM, New York, NY, 285--288. DOI:http://dx.doi.org/10.1145/2043932.2043984 Google ScholarDigital Library
- Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08). ACM, New York, NY, 426--434. DOI:http://dx.doi.org/10.1145/1401890.1401944 Google ScholarDigital Library
- Seung-Taek Park and Wei Chu. 2009. Pairwise preference regression for cold-start recommendation. In Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys’09). ACM, New York, NY, 21--28. DOI:http://dx.doi.org/10.1145/1639714.1639720 Google ScholarDigital Library
- Steffen Rendle. 2012. Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3, 3, Article 57 (May 2012), 22 pages. Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI’09). AUAI Press, Arlington, VA, 452--461. http://dl.acm.org/citation.cfm?id=1795114.1795167. Google ScholarDigital Library
- Manuel de Buenaga Rodríguez, Manuel J. Maña López, Alberto Díaz Esteban, and Pablo Gervás Gómez-Navarro. 2001. A user model based on content analysis for the intelligent personalization of a news service. In Proceedings of the 8th International Conference on User Modeling 2001 (UM’01). Springer-Verlag, London, 216--218. http://dl.acm.org/citation.cfm?id=647664.733412. Google ScholarDigital Library
- Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (WWW’01). ACM, New York, NY, 11. Google ScholarDigital Library
- Chong Wang and David M. Blei. 2011. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11). ACM, New York, NY, 448--456. DOI:http://dx.doi.org/10.1145/2020408.2020480 Google ScholarDigital Library
- Wen-tau Yih. 2009. Learning term-weighting functions for similarity measures. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2 (EMNLP’09). Google ScholarDigital Library
- Liang Zhang, Deepak Agarwal, and Bee-Chung Chen. 2011. Generalizing matrix factorization through flexible regression priors. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11). ACM, New York, NY, 13--20. DOI:http://dx.doi.org/10.1145/2043932.2043940 Google ScholarDigital Library
- Lanbo Zhang and Yi Zhang. 2010. Discriminative factored prior models for personalized content-based recommendation. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM’10). ACM, New York, NY, 1569--1572. DOI:http://dx.doi.org/10.1145/1871437.1871674 Google ScholarDigital Library
- Yi Zhang and Jonathan Koren. 2007. Efficient Bayesian hierarchical user modeling for recommendation system. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’07). 47--54. Google ScholarDigital Library
- Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web (WWW’05). ACM, New York, NY, 22--32. Google ScholarDigital Library
Index Terms
- User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items
Recommendations
A new similarity function for selecting neighbors for each target item in collaborative filtering
As one of the collaborative filtering (CF) techniques, memory-based CF technique which recommends items to users based on rating information of like-minded users (called neighbors) has been widely used and has also proven to be useful in many practices ...
Jointly modeling content, social network and ratings for explainable and cold-start recommendation
Model-based approach to collaborative filtering (CF), such as latent factor models, has improved both accuracy and efficiency of predictions on large and sparse dataset. However, most existing methods still face two major problems: (1) the ...
A hybrid recommendation system based on profile expansion technique to alleviate cold start problem
AbstractRecommender systems are one of the information filtering tools which can be employed to find interest items of users. Collaborative filtering is one of the recommendation methods to provide suggestions for target users based on the ratings of like-...
Comments