Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning in graphical models
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting Item Taxonomy for Solving Cold-Start Problem in Recommendation Making
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
The Unreasonable Effectiveness of Data
IEEE Intelligent Systems
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
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
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
Response prediction using collaborative filtering with hierarchies and side-information
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
RecDB in action: recommendation made easy in relational databases
Proceedings of the VLDB Endowment
GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Taxonomy discovery for personalized recommendation
Proceedings of the 7th 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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Recommender systems based on latent factor models have been effectively used for understanding user interests and predicting future actions. Such models work by projecting the users and items into a smaller dimensional space, thereby clustering similar users and items together and subsequently compute similarity between unknown user-item pairs. When user-item interactions are sparse (sparsity problem) or when new items continuously appear (cold start problem), these models perform poorly. In this paper, we exploit the combination of taxonomies and latent factor models to mitigate these issues and improve recommendation accuracy. We observe that taxonomies provide structure similar to that of a latent factor model: namely, it imposes human-labeled categories (clusters) over items. This leads to our proposed taxonomy-aware latent factor model (TF) which combines taxonomies and latent factors using additive models. We develop efficient algorithms to train the TF models, which scales to large number of users/items and develop scalable inference/recommendation algorithms by exploiting the structure of the taxonomy. In addition, we extend the TF model to account for the temporal dynamics of user interests using high-order Markov chains. To deal with large-scale data, we develop a parallel multi-core implementation of our TF model. We empirically evaluate the TF model for the task of predicting user purchases using a real-world shopping dataset spanning more than a million users and products. Our experiments demonstrate the benefits of using our TF models over existing approaches, in terms of both prediction accuracy and running time.