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CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
TLDR
In this paper we tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Expand
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List-wise learning to rank with matrix factorization for collaborative filtering
TLDR
A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF). Expand
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TFMAP: optimizing MAP for top-n context-aware recommendation
TLDR
In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios. Expand
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Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges
TLDR
We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. Expand
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Cross-Domain Collaborative Filtering with Factorization Machines
TLDR
We apply factorization machines to cross-domain recommendation in a way that allows them to incorporate user interaction patterns that are specific to particular types of items. Expand
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TRECVID 2016: Evaluating Video Search, Video Event Detection, Localization, and Hyperlinking
TLDR
The TREC Video Retrieval Evaluation (TRECVID) 2016 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Expand
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Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering
TLDR
We propose a novel algorithm, Tag-induced Cross-Domain Collaborative Filtering (TagCDCF), which exploits user-contributed tags that are common to multiple domains in order to establish the cross-domain links necessary for successfulCross-domain CF. Expand
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CARS2: Learning Context-aware Representations for Context-aware Recommendations
TLDR
We propose CARS2, a novel approach for learning context-aware representations for contextual-aware recommender systems. Expand
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Pairwise geometric matching for large-scale object retrieval
TLDR
We consider the pairwise geometric relations between correspondences and propose a strategy to incorporate these relations at significantly reduced computational cost, which makes it suitable for large-scale object retrieval. Expand
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Bayesian Personalized Ranking with Multi-Channel User Feedback
TLDR
We propose Multi-feedback Bayesian Personalized Ranking (MF-BPR), a pairwise method that exploits different types of feedback with an extended sampling method. Expand
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