基于属性图注意力网络的电影推荐模型

doi:10.11896/jsjkx.211100106

摘要/Abstract

摘要： 近年来,图网络被广泛应用于推荐领域并取得了较大进展,但是现有方法往往侧重于用户项目的交互建模,从而性能容易受到数据稀疏问题的限制。因此文中利用额外的属性信息,提出了一种基于属性图注意力网络的电影推荐模型。首先提出了一种基于注意力的GNN,采用显式反馈来计算实体和属性间的注意力得分,相比较使用拉普拉斯矩阵的聚合方式,能够更有效地区分不同属性对实体的影响,在属性和实体间信息聚合上更加有效。此外,由于不同实体受属性影响和行为影响的程度不同,文中设计了一种细粒度偏好融合策略,将属性群体偏好和个人行为偏好这两个方面的偏好更好地结合在一起,使实体的嵌入表示更加全面准确和个性化。在真实的数据集上进行实验,结果表明所提推荐方法充分利用属性图中蕴含的属性信息能够有效缓解数据稀疏问题,并且在电影推荐的两个评价指标召回率和nDCG上都明显优于其他基准算法。

关键词: 图神经网络, 属性图, 注意力机制, 显式反馈, 电影推荐

Abstract: In recent years,graph network has been widely used in the field of recommendation and made a great progress.How-ever,the existing methods tend to focus on the interaction modeling of user projects,so the performance is limited by the problem of data sparsity.Therefore,this paper proposes a movie recommendation model based on graph attention network of attribute graph by using additional attribute information.Firstly,an attention-based GNN is proposed,which uses explicit feed-back to calculate the attention score between entities and attributes.Compared with the aggregation method using Laplace matrix,it can distinguish the influence of different attributes on entities more effectively,and the information aggregation between attributes and entities can be more effective.In addition,different entities are affected differently by attributes and behaviors,a fine-grained pre-ference fusion strategy is designed in this paper to calculate a set of preference fusion weights for each entity to make the embedding representation of entities more accurate and personalized.Experimental results on real data set show that the recommendation method that makes full use of attribute information contained in attribute graph can effectively alleviate the problem of data sparsity and is significantly better than other basic algorithm in terms of recall rate and nDCG,two evaluation indexes of movie recommendation.

Key words: Graph neural network, Attribute graph, Attention mechanism, Explicit feed-back, Movie recommendation

中图分类号:

TP311

孙开伟, 刘松, 杜雨露. 基于属性图注意力网络的电影推荐模型[J]. 计算机科学, 2022, 49(11A): 211100106-8. https://doi.org/10.11896/jsjkx.211100106

SUN Kai-wei, LIU Song, DU Yu-lu. Movie Recommendation Model Based on Attribute Graph Attention Network[J]. Computer Science, 2022, 49(11A): 211100106-8. https://doi.org/10.11896/jsjkx.211100106

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