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xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance.

, , , , and . RecSys, page 431-434. ACM, (2013)

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Group recommendations with rank aggregation and collaborative filtering., , and . RecSys, page 119-126. ACM, (2010)Multi-dimensional Histograms with Tight Bounds for the Error., , and . IDEAS, page 105-112. IEEE Computer Society, (2006)GAPfm: optimal top-n recommendations for graded relevance domains., , , , and . CIKM, page 2261-2266. ACM, (2013)Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering., , , and . RecSys, page 79-86. ACM, (2010)Exploiting contextual information in recommender systems.. RecSys, page 295-298. ACM, (2008)xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance., , , , and . RecSys, page 431-434. ACM, (2013)CARS2: Learning Context-aware Representations for Context-aware Recommendations., , , , and . CIKM, page 291-300. ACM, (2014)Context-Dependent Recommendations with Items Splitting., and . IIR, volume 560 of CEUR Workshop Proceedings, page 71-75. CEUR-WS.org, (2010)Context-based splitting of item ratings in collaborative filtering., and . RecSys, page 245-248. ACM, (2009)CLiMF: Collaborative Less-Is-More Filtering., , , , , and . IJCAI, page 3077-3081. IJCAI/AAAI, (2013)