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Experimental evaluation of context-dependent collaborative filtering using item splitting

机译：使用项目拆分的上下文相关协作过滤的实验评估

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摘要

Collaborative Filtering (CF) computes recommendations by leveraging a historical data set of users' ratings for items. CF assumes that the users' recorded ratings can help in predicting their future ratings. This has been validated extensively, but in some domains the user's ratings can be influenced by contextual conditions, such as the time, or the goal of the item consumption. This type of contextual information is not exploited by standard CF models. This paper introduces and analyzes a novel technique for context-aware CF called Item Splitting. In this approach items experienced in two alternative contextual conditions are "split" into two items. This means that the ratings of a split item, e.g., a place to visit, are assigned (split) to two new fictitious items representing for instance the place in summer and the same place in winter. This split is performed only if there is statistical evidence that under these two contextual conditions the items ratings are different; for instance, a place may be rated higher in summer than in winter. These two new fictitious items are then used, together with the unaffected items, in the rating prediction algorithm. When the system must predict the rating for that "split" item in a particular contextual condition (e.g., in summer), it will consider the new fictitious item representing the original one in that particular contextual condition, and will predict its rating. We evaluated this approach on real world, and semi-synthetic data sets using matrix factorization, and nearest neighbor CF algorithms. We show that Item Splitting can be beneficial and its performance depends on the method used to determine which items to split. We also show that the benefit of the method is determined by the relevance of the contextual factors that are used to split.
机译：协作过滤（CF）通过利用项目用户评分的历史数据集来计算建议。 CF假定用户记录的收视率可以帮助预测其未来收视率。这已经得到了广泛的验证，但是在某些领域中，用户的评分可能会受到上下文条件（例如时间或项目消费目标）的影响。标准的CF模型不使用这种类型的上下文信息。本文介绍并分析了一种用于上下文感知CF的新技术，称为项拆分。在这种方法中，将在两个替代上下文条件下遇到的项目“拆分”为两个项目。这意味着将拆分项的等级（例如访问地点）分配（拆分）给两个新的虚拟项，例如代表夏季的地点和冬季的相同地点。仅当有统计证据表明在这两个上下文条件下项目等级不同时，才执行此拆分；例如，夏季某个地方的评价可能高于冬季。然后，在评级预测算法中将这两个新的虚拟项目与未受影响的项目一起使用。当系统必须在特定上下文条件下（例如，夏季）预测该“拆分”项目的评级时，它将考虑代表该特定上下文条件下的原始虚拟项目的新虚拟项目，并预测其评级。我们在现实世界和使用矩阵分解和最近邻CF算法的半合成数据集上评估了该方法。我们证明了项目拆分可能是有益的，其性能取决于用于确定要拆分的项目的方法。我们还表明，该方法的好处取决于用于拆分的上下文因素的相关性。

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