Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.


翻译:以项目为基础的合作过滤器( ICF 短于 ICF ) 已经在行业的推荐系统中被广泛采用, 因为它在用户兴趣建模和在线个人化的方便度方面表现得很强。 通过用用户消费的物品构建用户概况, ICF 推荐了类似于用户配置的物品。 随着近年来机器学习的普及, 通过从数据中学习项目相似( 或表示), 已经为ICF 制定了重要程序。 然而, 我们争论说, 大多数现有工程只考虑项目之间的线性和浅度关系, 不足以捕捉用户复杂的决策程序。 在这项工作中, 我们提出一个更清晰的ICF 解决方案, 通过计算项目之间的非线性和更高级关系。 例如, 通过计算项目之间的非线性和更高级关系, 我们提出一个更清晰的ICF 解决方案解决方案。

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IFIP TC13 Conference on Human-Computer Interaction是人机交互领域的研究者和实践者展示其工作的重要平台。多年来,这些会议吸引了来自几个国家和文化的研究人员。官网链接:http://interact2019.org/
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