Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Content-based filtering and personalization using structured metadata
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal collaborative filtering with adaptive neighbourhoods
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Mining mood-specific movie similarity with matrix factorization for context-aware 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
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
Location-based recommendation system using Bayesian user's preference model in mobile devices
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
gSVD++: supporting implicit feedback on recommender systems with metadata awareness
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Optimizing top-n collaborative filtering via dynamic negative item sampling
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Which app will you use next?: collaborative filtering with interactional context
Proceedings of the 7th ACM conference on Recommender systems
Hybrid recommenders: incorporating metadata awareness into latent factor models
Proceedings of the 19th Brazilian symposium on Multimedia and the web
A survey of music similarity and recommendation from music context data
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Digital music has experienced a quite fascinating transformation during the past decades. Thousands of people share or distribute their music collections on the Internet, resulting in an explosive increase of information and more user dependence on automatic recommender systems. Though there are many techniques such as collaborative filtering, most approaches focus mainly on users' global behaviors, neglecting local actions and the specific properties of music. In this paper, we propose a simple and effective local implicit feedback model mining users' local preferences to get better recommendation performance in both rating and ranking prediction. Moreover, we design an efficient training algorithm to speed up the updating procedure, and give a method to find the most appropriate time granularity to assist the performance. We conduct various experiments to evaluate the performance of this model, which show that it outperforms baseline model significantly. Integration with existing temporal models achieves a great improvement compared to the reported best single model for Yahoo! Music.