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Temporal Item Embedding with Static Similarity Regularization for Sequential Recommendation

机译：具有静态相似性正则化的时间项嵌入用于顺序推荐

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摘要

Recommender systems have attracted a significant amount of research interests in recent years. Traditional methods such as content-based approaches and collaborative filtering approaches mainly focus on modeling the general user preference by using the user's whole purchase history. However, except for user preference, the sequential information should also be considered because human behavior exhibits sequential patterns. The methods considering both sequential item relationship and user preference are called sequential recommendation methods, which are mostly base on Markov Chains. Due to the difficulty of parameter estimation, most prior work only considers the latest interaction, which is insufficient according to our observations. To that end, in this paper, we propose a temporal item embedding method based on word2vec framework to model long purchase history, with each purchased item regarded as a word in sentence. Meanwhile, inspired by the collaborative filtering in traditional recommendations, we assume that similar items should have similar embeddings and propose static similarity regularization (SSR) in recommendation. The regularized item embedding can capture user preference and sequential item relationship simultaneously. Experiments on real-world datasets show that the proposed approach outperforms a spectrum of state-of-the-art algorithms.
机译：近年来，推荐系统吸引了大量研究兴趣。诸如基于内容的方法和协作过滤方法之类的传统方法主要集中于通过使用用户的整个购买历史来对一般用户的偏好进行建模。但是，除了用户喜好外，还应考虑顺序信息，因为人类行为表现出顺序模式。同时考虑顺序项目关系和用户偏好的方法称为顺序推荐方法，这些方法主要基于马尔可夫链。由于参数估计的困难，大多数先前的工作仅考虑最新的交互作用，根据我们的观察，这是不够的。为此，在本文中，我们提出了一种基于word2vec框架的时态项目嵌入方法，用于建模长期的购买历史，其中每个购买的项目都视为句子中的单词。同时，受传统推荐中协同过滤的启发，我们假设相似的项目应具有相似的嵌入，并在推荐中提出静态相似性正则化（SSR）。正规项目嵌入可以同时捕获用户偏好和顺序项目关系。在真实数据集上进行的实验表明，该方法优于一系列最新算法。

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