图 书 馆
个性化标签推荐的成对交互张量分解Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation |
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| 课程网址: | http://videolectures.net/wsdm2010_rendle_pit/ |
| 主讲教师: | Steffen Rendle |
| 开课单位: | 谷歌公司 |
| 开课时间: | 2010-04-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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 run- time 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. |
| 关 键 词: | 计算机科学; 语义网; 注释 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-30：王勇彬(课程编辑志愿者) |
| 阅读次数: | 359 |