MCN4Rec:用于下一个地点推荐的多层次协作神经网络

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shuzhe Li, Wei Chen, Bin Wang, Chao Huang, Yanwei Yu, Junyu Dong
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引用次数: 0

摘要

下一步位置推荐在各种基于位置的服务中发挥着重要作用,为用户和服务提供商带来巨大价值。现有方法通常通过明确的时间间隔对时间依赖性进行建模,或从具有丰富上下文信息的定制兴趣点(POI)图中学习表示法,以捕捉 POI 之间的顺序模式。然而,由于需要综合考虑用户偏好、空间位置、时间背景、活动类别语义和时间关系等各种因素,而大多数研究又缺乏对协作信号的充分考虑,因此这个问题显得非常复杂。为此,我们提出了一种新颖的用于下一个位置推荐的多层次协作神经网络(MCN4Rec)。具体来说,我们设计了一种多层次视图表示学习,通过层次对比学习从本地和全局角度协作学习表示,以捕捉用户、POI、时间和活动类别之间复杂的异构关系。然后将因果编码器-解码器应用于签到序列的学习表示,以推荐下一个地点。在四个真实世界签到移动数据集上进行的广泛实验表明,我们的模型在推荐下一个地点方面明显优于现有的最先进基线模型。消融研究进一步验证了所设计子模块的协作优势。源代码见 https://github.com/quai-mengxiang/MCN4Rec。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MCN4Rec: Multi-Level Collaborative Neural Network for Next Location Recommendation

Next location recommendation plays an important role in various location-based services, yielding great value for both users and service providers. Existing methods usually model temporal dependencies with explicit time intervals or learn representation from customized point of interest (POI) graphs with rich context information to capture the sequential patterns among POIs. However, this problem is perceptibly complex because various factors, e.g., users’ preferences, spatial locations, time contexts, activity category semantics, and temporal relations, need to be considered together, while most studies lack sufficient consideration of the collaborative signals. Toward this goal, we propose a novel Multi-Level Collaborative Neural Network for next location Recommendation (MCN4Rec). Specifically, we design a multi-level view representation learning with level-wise contrastive learning to collaboratively learn representation from local and global perspectives to capture complex heterogeneous relationships among user, POI, time, and activity categories. Then a causal encoder-decoder is applied to the learned representations of check-in sequences to recommend the next location. Extensive experiments on four real-world check-in mobility datasets demonstrate that our model significantly outperforms the existing state-of-the-art baselines for the next location recommendation. Ablation study further validates the benefits of the collaboration of the designed sub-modules. The source code is available at https://github.com/quai-mengxiang/MCN4Rec.

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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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