Abstract
Recommendation for user generated content sites has gained significant attention. To satisfy the niche tastes of users, product recommendation poses more challenges due to the data sparsity issue. This work is motivated by a real world online video recommendation problem, where the click records database suffers from s-parseness of video inventory and video tags. Targeting the long tail phenomena of user behavior and sparsity of item features, we propose a personalized compound recommendation framework for online video recommendation called Dirichlet mixture probit model for information scarcity (DPIS). Assuming that each record is generated from a representation of user preferences, DPIS is a probit classifier utilizing record topical clustering on the user part for recommendation. As demonstrated by the real-world application, the proposed DPIS achieves better performance than traditional methods.
Original language | English |
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Title of host publication | CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management |
Publisher | Association for Computing Machinery |
Pages | 1993-1996 |
Number of pages | 4 |
Volume | 24-28-October-2016 |
ISBN (Electronic) | 9781450340731 |
DOIs | |
Publication status | Published - 24 Oct 2016 |
Event | 25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States Duration: 24 Oct 2016 → 28 Oct 2016 |
Conference
Conference | 25th ACM International Conference on Information and Knowledge Management, CIKM 2016 |
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Country | United States |
City | Indianapolis |
Period | 24/10/16 → 28/10/16 |
Keywords
- Bayesian approach
- Recommender system
- Topic model
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Decision Sciences(all)