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On-line Weighted Matrix Factorization for TV Program Recommendation

机译:电视节目推荐的在线加权矩阵分解

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摘要

Matrix Factorization (MF) is known as an effective technique in collaborative filtering for recommendation. The MF approaches have often been applied for movie recommender systems which have user rating data. However, they cannot effectively be applicable for TV program recommender systems because (ⅰ) explicit rating values are not available; (ⅱ) many TV programs are broadcast under single TV program titles such as News, Shows, Dramas etc.; and (ⅲ) the preferences of TV viewers on TV programs are subject to change in time. Therefore, in this paper, we propose an MF technique that considers such problems, thus making the MF technique suitably applicable for TV domain. We also present experimental results to show the effectiveness of our proposed extended MF technique.
机译:矩阵分解(MF)是协作过滤推荐中的有效技术。 MF方法通常已应用于具有用户评分数据的电影推荐系统。但是,它们不能有效地应用于电视节目推荐系统,因为(ⅰ)没有明确的等级值; (ⅱ)许多电视节目以单个电视节目标题(例如新闻,节目,戏剧等)播放; (ⅲ)电视观众对电视节目的偏好会随时间而改变。因此,在本文中,我们提出了一种考虑了此类问题的MF技术,从而使MF技术适用于电视领域。我们还提出了实验结果,以证明我们提出的扩展MF技术的有效性。

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