Factorization Models for Context-/Time-Aware Movie Recommendations

被引：42

作者：

Gantner, Zeno ^{[1
]}

Rendle, Steffen ^{[1
]}

Schmidt-Thieme, Lars ^{[1
]}

机构：

[1] Univ Hildesheim, Machine Learning Grp, Hildesheim, Germany

来源：

PROCEEDINGS OF THE RECSYS'2010 ACM CHALLENGE ON CONTEXT-AWARE MOVIE RECOMMENDATION (CAMRA2010) | 2010年

关键词：

context-aware recommender systems; item recommendation; temporal; tensor factorization; matrix factorization;

D O I：

10.1145/1869652.1869654

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the scope of the Challenge on Context-aware Movie Recommendation (CAMRa2010), context can mean temporal context (Task 1), mood (Task 2), or social context (Task 3). We suggest to use Pairwise Interaction Tensor Factorization (PITF), a method used for personalized tag recommendation, to model the temporal (week) context in Task 1 of the challenge. We also present an extended version of PITF that handles the week context in a smoother way. In the experiments, we compare PITF against di ff erent item recommendation baselines that do not take context into account, and a non-personalized context-aware baseline.

引用

页码：14 / 19

页数：6

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