Pairwise interaction tensor factorization for personalized tag recommendation
Pairwise interaction tensor factorization for personalized tag recommendation
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DOI：
10.1145/1718487.1718498
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发表时间：
2010-02
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影响因子：
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通讯作者：
Steffen Rendle;L. Schmidt-Thieme
中科院分区：
文献类型：
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作者：
Steffen Rendle;L. Schmidt-Thieme
标记在许多最近的网站中都起着重要作用。推荐系统可以帮助用户向用户推荐他可能要使用的标签标签标签。基于Tucker分解（TD）模型的分解模型已被证明可以提供高质量的标签建议，超过其他方法，例如Pagerank，Folkrank，协作过滤等。TD模型的问题是立方核心张量，导致在立方运行时在立方运行时产生。预测和学习的分解维度。在本文中，我们介绍了分解模型PITF（成对相互作用张量分解），该模型是TD模型的特殊情况，具有用于学习和预测的线性运行时。 PITF明确对用户，项目和标签之间的成对交互作用。该模型是通过改编的贝叶斯个性化排名（BPR）标准来学习的，该标准最初是为了项目推荐而引入的。从经验上讲，我们在现实世界数据集上表明，该模型在运行时的表现优于TD，甚至可以实现更好的预测质量。除我们的实验室实验外，PITF还赢得了基于图的标签建议的ECML/PKDD Discovery Challenge 2009。
Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning. In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.