Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Yuan Lin, Hongfei Lin
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引用次数: 0

Abstract

Session-based recommendation intends to predict next purchased items based on anonymous behavior sequences. Numerous economic studies have revealed that item price is a key factor influencing user purchase decisions. Unfortunately, existing methods for session-based recommendation only aim at capturing user interest preference, while ignoring user price preference. Actually, there are primarily two challenges preventing us from accessing price preference. Firstly, the price preference is highly associated to various item features ( i.e., category and brand), which asks us to mine price preference from heterogeneous information. Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling. To handle above challenges, we propose a novel approach Bi-Preference Learning Heterogeneous Hypergraph Networks (BiPNet) for session-based recommendation. Specifically, the customized heterogeneous hypergraph networks with a triple-level convolution are devised to capture user price and interest preference from heterogeneous features of items. Besides, we develop a Bi-Preference Learning schema to explore mutual relations between price and interest preference and collectively learn these two preferences under the multi-task learning architecture. Extensive experiments on multiple public datasets confirm the superiority of BiPNet over competitive baselines. Additional research also supports the notion that the price is crucial for the task.
基于会话推荐的双偏好学习异构超图网络
基于会话的推荐旨在根据匿名行为序列预测下一个购买的物品。大量经济学研究表明,商品价格是影响用户购买决策的关键因素。遗憾的是,现有的基于会话的推荐方法只关注用户的兴趣偏好,而忽略了用户的价格偏好。实际上,阻碍我们获得价格优惠的主要有两个挑战。首先,价格偏好与各种商品特征(即品类和品牌)高度相关,这就要求我们从异构信息中挖掘价格偏好。其次,价格偏好和兴趣偏好是相互依赖的,共同决定了用户的选择,因此我们需要在意向建模中同时考虑价格偏好和兴趣偏好。为了应对上述挑战,我们提出了一种新的基于会话的推荐方法——双偏好学习异构超图网络(BiPNet)。具体而言,设计了具有三层卷积的定制异构超图网络,以从物品的异构特征中捕获用户价格和兴趣偏好。此外,我们开发了一个双偏好学习模式来探索价格偏好和兴趣偏好之间的相互关系,并在多任务学习架构下集体学习这两种偏好。在多个公共数据集上进行的大量实验证实了BiPNet优于竞争基线的优势。额外的研究也支持了价格对任务至关重要的观点。
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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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