Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Tied boltzmann machines for cold start recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Large-scale collaborative prediction using a nonparametric random effects model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Scalable distributed inference of dynamic user interests for behavioral targeting
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy
Proceedings of the fifth ACM conference on Recommender systems
Scalable inference in latent variable models
Proceedings of the fifth ACM international conference on Web search and data mining
Improving pairwise learning for item recommendation from implicit feedback
Proceedings of the 7th ACM international conference on Web search and data mining
Modeling contextual agreement in preferences
Proceedings of the 23rd international conference on World wide web
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Items in recommender systems are usually associated with annotated attributes: for e.g., brand and price for products; agency for news articles, etc. Such attributes are highly informative and must be exploited for accurate recommendation. While learning a user preference model over these attributes can result in an interpretable recommender system and can hands the cold start problem, it suffers from two major drawbacks: data sparsity and the inability to model random effects. On the other hand, latent-factor collaborative filtering models have shown great promise in recommender systems; however, its performance on rare items is poor. In this paper we propose a novel model LFUM, which provides the advantages of both of the above models. We learn user preferences (over the attributes) using a personalized Bayesian hierarchical model that uses a combination(additive model) of a globally learned preference model along with user-specific preferences. To combat data-sparsity, we smooth these preferences over the item-taxonomy using an efficient forward-filtering and backward-smoothing inference algorithm. Our inference algorithms can handle both discrete attributes (e.g., item brands) and continuous attributes (e.g., item prices). We combine the user preferences with the latent-factor models and train the resulting collaborative filtering system end-to-end using the successful BPR ranking algorithm. In our extensive experimental analysis, we show that our proposed model outperforms several commonly used baselines and we carry out an ablation study showing the benefits of each component of our model.