Yuchen Liu
ABSTRACT
This paper introduces human curation signals and demonstrates incorporating human curation signals improves the relevance of state-of-art recommendation system models by up to 30% by experiments on a large-scale Pinterest dataset.
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM, 2008. Google Scholar
Digital Library
- P. Resnick et al. Grouplens: an open architecture for collaborative filtering of netnews. In CSCW, 1994. Google ScholarDigital Library
- B. Sarwar et al. Item-based collaborative filtering recommendation algorithms. In WWW, 2001. Google ScholarDigital Library
- X. Su and T. Khoshgoftaar. A survey of collaborative filtering techniques. Adv. in Artif. Intell., 2009. Google ScholarDigital Library
Index Terms
- Power of Human Curation in Recommendation System
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