Item-based collaborative filtering (ICF) has been widely used in industrial applications such as recommender system and online advertising. It models users' preference on target items by the items they have interacted with. Recent models use methods such as attention mechanism and deep neural network to learn the user representation and scoring function more accurately. However, despite their effectiveness, such models still overlook a problem that performance of ICF methods heavily depends on the quality of item representation especially the target item representation. In fact, due to the long-tail distribution in the recommendation, most item embeddings can not represent the semantics of items accurately and thus degrade the performance of current ICF methods. In this paper, we propose an enhanced representation of the target item which distills relevant information from the co-occurrence items. We design sampling strategies to sample fix number of co-occurrence items for the sake of noise reduction and computational cost. Considering the different importance of sampled items to the target item, we apply attention mechanism to selectively adopt the semantic information of the sampled items. Our proposed Co-occurrence based Enhanced Representation model (CER) learns the scoring function by a deep neural network with the attentive user representation and fusion of raw representation and enhanced representation of target item as input. With the enhanced representation, CER has stronger representation power for the tail items compared to the state-of-the-art ICF methods. Extensive experiments on two public benchmarks demonstrate the effectiveness of CER.

中文翻译：

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按项目表示项目：建议的目标项目的增强表示形式

基于项目的协作过滤（ICF）已被广泛用于工业应用，例如推荐系统和在线广告。它通过与用户交互的项目来模拟用户对目标项目的偏好。最近的模型使用诸如注意力机制和深度神经网络之类的方法来更准确地学习用户表示和评分功能。然而，尽管它们的模型有效，但是这些模型仍然忽略了一个问题，即ICF方法的性能在很大程度上取决于项目表示的质量，尤其是目标项目表示的质量。实际上，由于推荐中的长尾分布，大多数项嵌入无法准确表示项的语义，因此会降低当前ICF方法的性能。在本文中，我们建议对目标项目进行增强的表示，以从共现项目中提取相关信息。为了减少噪声和降低计算成本，我们设计了采样策略以对共现项的固定数量进行采样。考虑到被采样项对目标项的重要性不同，我们应用注意力机制来选择性地采用被采样项的语义信息。我们提出的基于共现的增强表示模型（CER）通过深层神经网络学习评分功能，该网络具有专心的用户表示以及原始表示和目标项目的增强表示的融合作为输入。与最新的ICF方法相比，借助增强的表示，CER对尾项具有更强的表示能力。