Session: Algorithms

Chair: Domonkos Tikk
Date: Monday, October 24, 14:00-15:45

• Generalizing matrix factorization through flexible regression priors

  by Liang Zhang, Deepak Agarwal, Bee-Chung Chen

  Predicting user “ratings” on items is a crucial task in recommender systems. Matrix factorization methods that computes a low-rank approximation of the incomplete user-item rating matrix provide state-of-the-art performance, especially for users and items with several past ratings (warm starts). However, it is a challenge to generalize such methods to users and items with few or no past ratings (cold starts). Prior work have generalized matrix factorization to include both user and item features for performing better regularization of factors as well as provide a model for smooth transition from cold starts to warm starts. However, the features were incorporated via linear regression on factor estimates. In this paper, we generalize this process to allow for arbitrary regression models like decision trees, boosting, LASSO, etc. The key advantage of our approach is the ease of computing — any new regression procedure can be incorporated by “plugging” in a standard regression routine into a few intermediate steps of our model fitting procedure. With this flexibility, one can leverage a large body of work on regression modeling, variable selection, and model interpretation. We demonstrate the usefulness of this generalization using the MovieLens and Yahoo! Buzz datasets.

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• Modeling item selection and relevance for accurate recommendations: a bayesian approach

  by Nicola Barbieri, Gianni Costa, Giuseppe Manco, Riccardo Ortale

  We propose a bayesian probabilistic model for explicit preference data. The model introduces a generative process, which takes into account both item selection and rating emission to gather into communities those users who experience the same items and tend to adopt the same rating pattern. Each user is modeled as a random mixture of topics, where each topic is characterized by a distribution modeling the popularity of items within the respective user-community and by a distribution over preference values for those items. The proposed model can be associated with a novel item-relevanceranking criterion, which is based both on item popularity and user’s preferences. We show that the proposed model, equipped with the new ranking criterion, outperforms state-of-art approaches in terms of accuracy of the recommendation list provided to users on standard benchmark datasets.

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• Shared collaborative filtering

  by Yu Zhao, Xinping Feng, Jianqiang Li, Bo Liu

  Traditional collaborative filtering (CF) methods suffer from sparse or even cold-start problems, especially for new established recommenders. However, since there are now quite a few recommender systems already existing in good working order, their data should be valuable to the new-start recommenders. This paper proposes shared collaborative filtering approach to leverage the data from other parties (contributor party) to improve own (beneficiary party’s) CF performance, and at the same time the privacy of other parties cannot be compromised. Item neighborhood list is chosen as the shared data from the contributor party with considering differential privacy. And an innovative algorithm called neighborhood boosting is proposed to make the beneficiary party leverage the shared data. MovieLens and Netflix data sets are considered as two parties to simulate and evaluate the proposed shared CF approach. The experiment results validate the positive effects of shared CF for increasing the recommendation accuracy of the beneficiary party. Especially when the beneficiary party’s data is quite sparse, the performance can be increased by around 10%. The experiments also show that shared CF even outperforms the methods that incorporate the detailed original rating scores of the contributor party without considering the privacy issues. The proposed shared CF approach obtains a win-win situation for both performance and privacy.

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• Wisdom of the better few: cold start recommendation via representative based rating elicitation

  by Nathan Liu, Xiangrui Meng, Chao Liu

  Recommender systems have to deal with the cold start problem as new users and/or items are always present. Rating elicitation is a common approach for handling cold start. However, there still lacks a principled model for guiding how to select the most useful ratings. In this paper, we propose a principled approach to identify representative users and items using representative-based matrix factorization. Not only do we show that the selected representatives are superior to other competing methods in terms of achieving good balance between coverage and diversity, but we also demonstrate that ratings on the selected representatives are much more useful for making recommendations (about 10% better than competing methods). In addition to illustrating how representatives help solve the cold start problem, we also argue that the problem of finding representatives itself is an important problem that would deserve further investigations, for both its practical values and technical challenges.

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