Metric Learning For Context-Aware Recommender Systems

Firat Ismailoglu
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Abstract

Context-Aware Recommender Systems (CARS) refer to recommender systems that can incorporate side information regarding to users, items and ratings. In the present study, we are concerned with CARS, where the side information is provided in the form of item-attribute matrix with entries indicating whether an item has an attribute. We propose to multiply this matrix with user-item rating matrix to represent the the users in the attribute space of the items. We then apply a popular metric learning method, specifically Mahalanobis Metric Learning (MMC), in the attribute space to calculate the distances between the users and their favorite items as less as possible. We recommend the n items that are closest to the users based on these calculations. We verify the effectiveness of the proposed method on two famous MovieLens datasets that differ in size showing that using metric learning increases the success of CARS up to 7% in comparison with using the traditional cosine similarity.
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上下文感知推荐系统的度量学习
上下文感知推荐系统(CARS)指的是可以包含有关用户、商品和评级的附加信息的推荐系统。在本研究中,我们关注的是CARS,其中的副信息以项目-属性矩阵的形式提供,其中的条目表示项目是否具有属性。我们建议将该矩阵与用户-物品评价矩阵相乘来表示物品属性空间中的用户。然后,我们在属性空间中应用一种流行的度量学习方法,特别是Mahalanobis度量学习(MMC),以尽可能少地计算用户与他们喜欢的物品之间的距离。根据这些计算,我们推荐最接近用户的n个项目。我们在两个著名的大小不同的MovieLens数据集上验证了所提出方法的有效性,结果表明,与使用传统的余弦相似度相比,使用度量学习将CARS的成功率提高了7%。
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