Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Major components of the gravity recommendation system
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Recommending new movies: even a few ratings are more valuable than metadata
Proceedings of the third ACM conference on Recommender systems
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
Proceedings of the third ACM conference on Recommender systems
RecSys'09 workshop 3: workshop on context-aware recommender systems (CARS-2009)
Proceedings of the third ACM conference on Recommender systems
Context-aware citation recommendation
Proceedings of the 19th international conference on World wide web
Fast als-based matrix factorization for explicit and implicit feedback datasets
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the fourth ACM conference on Recommender systems
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
Adapting neighborhood and matrix factorization models for context aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Applications of the conjugate gradient method for implicit feedback collaborative filtering
Proceedings of the fifth ACM conference on Recommender systems
Context-aware item-to-item recommendation within the factorization framework
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
Which app will you use next?: collaborative filtering with interactional context
Proceedings of the 7th ACM conference on Recommender systems
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Albeit the implicit feedback based recommendation problem--when only the user history is available but there are no ratings--is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS applies a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate various contextual information into the model while maintaining its computational efficiency. We present two context-aware implementation variants of iTALS. The first incorporates seasonality and enables to distinguish user behavior in different time intervals. The other views the user history as sequential information and has the ability to recognize usage pattern typical to certain group of items, e.g. to automatically tell apart product types that are typically purchased repetitively or once. Experiments performed on five implicit datasets (LastFM 1K, Grocery, VoD, and "implicitized" Netflix and MovieLens 10M) show that by integrating context-aware information with our factorization framework into the state-of-the-art implicit recommender algorithm the recommendation quality improves significantly.