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Bayesian pairwise learning to rank via one-class collaborative filtering
- Source :
- Neurocomputing. 367:176-187
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- With the ever-growing scale of social websites and online transactions, in past decade, Recommender System (RS) has become a crucial tool to overcome information overload, due to its powerful capability in information filtering and retrieval. Traditional rating prediction based RS could learn user’s preference according to the explicit feedback, however, such numerical user-item ratings are always unavailable in real life. By contrast, pairwise learning algorithms could directly optimize for ranking and provide personalized recommendation from implicit feedback, although suffering from such data sparsity and slow convergence problems. Motivated by these, in this article, a novel collaborative pairwise learning to rank method referred to as BPLR is proposed, which aims to improve the performance of personalized ranking from implicit feedback. To this end, BPLR tries to partition items into positive feedback, potential feedback and negative feedback, and takes account of the neighborhood relationship between users as well as the item similarity while deriving the potential candidates, moreover, a dynamic sampling strategy is designed to reduce the computational complexity and speed up model training. Empirical experiments over four real world datasets certificate the effectiveness and efficiency of BPLR, which could speed up convergence, and outperform state-of-the-art algorithms significantly in personalized top-N recommendation.
- Subjects :
- 0209 industrial biotechnology
Computer science
business.industry
Cognitive Neuroscience
Rank (computer programming)
Bayesian probability
02 engineering and technology
Recommender system
Machine learning
computer.software_genre
Computer Science Applications
Ranking (information retrieval)
020901 industrial engineering & automation
Ranking
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Collaborative filtering
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 367
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........1a1bdb4a8f93d5865184210d9f95d3b8