Personalized Recommendation Considering Secondary Implicit Feedback

Siyuan Liu, Qiong Wu, C. Miao
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引用次数: 5

Abstract

In e-commerce, recommendation is an essential feature to provide users with potentially interesting items to purchase. However, people are often faced with an unpleasant situation, where the recommended items are simply the ones similar to what they have purchased previously. One of the main reasons is that existing recommender systems in e-commerce mainly utilize primary implicit feedback (i.e., purchase history) for recommendation. Little attention has been paid to secondary implicit feedback (e.g., viewing items, adding items to shopping cart, adding items to favorite list, etc), which captures users' potential interests that may not be reflected in their purchase history. We therefore propose a personalized recommendation approach to combine the primary and secondary implicit feedback to generate the recommendation list, which is optimized towards a Bayesian objective criterion for personalized ranking. Experiments with a large-scale real-world e-commerce dataset show that the proposed approach presents a superior performance in comparison with the state-of-the-art baselines.
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考虑二次隐式反馈的个性化推荐
在电子商务中,推荐是为用户提供潜在感兴趣的商品的基本功能。然而,人们经常会面临一种不愉快的情况,即推荐的商品与他们以前购买的商品相似。其中一个主要原因是现有的电子商务推荐系统主要利用初级隐式反馈(即购买历史)进行推荐。次要的隐性反馈(例如,查看商品、将商品添加到购物车、将商品添加到收藏列表等)很少受到关注,这些反馈捕捉了用户的潜在兴趣,而这些兴趣可能没有反映在他们的购买历史中。因此,我们提出了一种个性化推荐方法,将主、次隐式反馈结合起来生成推荐列表,并针对个性化排名的贝叶斯客观标准进行优化。大规模真实电子商务数据集的实验表明,与最先进的基线相比,所提出的方法具有优越的性能。
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