Next-item Recommendation with Sequential Hypergraphs
Next-item Recommendation with Sequential Hypergraphs
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DOI：
10.1145/3397271.3401133
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Jianling Wang;Kaize Ding;Liangjie Hong;Huan Liu;James Caverlee
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文献类型：
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作者：
Jianling Wang;Kaize Ding;Liangjie Hong;Huan Liu;James Caverlee
下一项推荐系统越来越受到关注，它通过用户的连续交互来推断动态的用户偏好。虽然一个物品的语义可能会随着时间和用户的不同而改变，但短期内由用户交互所定义的物品相关性可以被提取出来以捕捉这种变化，并有助于揭示动态的用户偏好。因此，我们有动力开发一种由序列超图驱动的新型下一项推荐框架。具体而言，该框架：（i）采用超图来表示短期物品相关性，并应用多个卷积层来捕捉超图中的多阶连接；（ii）通过残差门控层对不同时间段之间的连接进行建模；（iii）配备一个融合层，在将每个交互的表示输入到自注意力层进行动态用户建模之前，将动态物品嵌入和短期用户意图都纳入其中。通过在来自电子商务网站亚马逊和Etsy以及信息共享平台Goodreads的数据集上进行的实验，所提出的模型在预测每个用户的下一个感兴趣的物品方面能够显著优于现有技术水平。
There is an increasing attention on next-item recommendation systems to infer the dynamic user preferences with sequential user interactions. While the semantics of an item can change over time and across users, the item correlations defined by user interactions in the short term can be distilled to capture such change, and help in uncovering the dynamic user preferences. Thus, we are motivated to develop a novel next-item recommendation framework empowered by sequential hypergraphs. Specifically, the framework: (i) adopts hypergraph to represent the short-term item correlations and applies multiple convolutional layers to capture multi-order connections in the hypergraph; (ii) models the connections between different time periods with a residual gating layer; and (iii) is equipped with a fusion layer to incorporate both the dynamic item embedding and short-term user intent to the representation of each interaction before feeding it into the self-attention layer for dynamic user modeling. Through experiments on datasets from the ecommerce sites Amazon and Etsy and the information sharing platform Goodreads, the proposed model can significantly outperform the state-of-the-art in predicting the next interesting item for each user.