结合注意力CNN与GNN的信息融合推荐方法
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钱忠胜(1977-),男,博士,教授,博士生导师,CCF专业会员,主要研究领域为推荐系统,机器学习,算法设计,软件工程;赵畅(1995-),女,硕士,主要研究领域为推荐系统,机器学习;俞情媛(1997-),女,博士生,主要研究领域为软件工程,机器学习;李端明(1995-),男,硕士,主要研究领域为推荐系统,机器学习

通讯作者:

钱忠胜,changesme@163.com

中图分类号:

TP391

基金项目:

国家自然科学基金(61762041);江西省自然科学基金(20181BAB202009);江西省教育厅科技重点项目(GJJ180250)


Information Fusion Recommendation Approach Combining Attention CNN and GNN
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Fund Project:

National Natural Science Foundation of China (61762041); Jiangxi Provincial Natural Science Foundation of China (20181BAB202009); Key Project of Science and Technology of Jiangxi Provincial Department of Education of China (GJJ180250)

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    摘要:

    稀疏性问题一直是推荐系统面临的主要挑战,而信息融合推荐可以利用用户的评论、评分以及信任等信息发掘用户的偏好来缓解这一问题,从而为目标用户生成相应的推荐.用户、项目信息的充分学习是构建一个成功推荐系统的关键.但不同用户对不同项目有不同的偏好,且用户的兴趣偏好及社交圈是动态变化的.提出一种结合深度学习与信息融合的推荐方法来解决稀疏性等问题.特别地,构建了一种新的深度学习模型——结合注意力卷积神经网络(attention CNN)与图神经网络(GNN)的信息融合推荐模型ACGIF.首先,在CNN中加入注意力机制来处理评论信息,从评论信息中学习用户和项目的个性化表示.根据评论编码学习评论表示,通过用户/项目编码学习评论中用户/项目表示.加入个性化注意力机制来筛选不同重要性级别的评论.然后,利用GNN来处理评分和信任信息.对于每个用户来说,扩散过程从最初的嵌入开始,融合相关特性和捕获潜在行为偏好的自由用户潜在向量.设计了一个分层的影响传播结构,以模拟用户的潜在嵌入如何随着社交扩散过程的继续而演变.最后,对前两部分得到的用户对项目的偏好向量进行加权融合,获得最终的用户对于项目的偏好向量.在4组公开数据集上,以推荐结果的MAERMSE作为评估指标进行了实验验证.结果表明,与现有的7个典型推荐模型相比,所提模型的推荐效果和运行时间均占优.

    Abstract:

    The sparsity has always been a primary challenge for recommendation system, and information fusion recommendation can alleviate this problem by exploiting user preference through their comments, ratings, and trust information, so as to generate corresponding recommendations for target users. Full learning of user and item information is the key to build a successful recommendation system. Different users have different preferences for various items, and users’ interest preferences and social circle are changeable dynamically. A recommendation method combining deep learning and information fusion is proposed to solve the problem of sparsity. Particularly, a new deep learning model named information fusion recommendation model combining attention CNN and GNN (ACGIF for short), is constructed. First, attention mechanism is added to the CNN to process the comment information and learn the personalized representation of users and items from the comment information. It learns the comment representation based on comment coding, and learns the user/item representation in the comment through user/item coding. It adds personalized attention mechanism to filter comments with different levels of importance. Then, the rating and trust information are processed through the GNN. For each user, the diffusion process begins with the initial embedding, combining the relevant features and the free user potential vectors that capture the potential behavioral preferences. A layered influence propagation structure is designed to simulate how the user’s potential embedding evolves as the social diffusion process continues. Finally, the preference vector of the user for the item obtained from the first two parts is weighted and fused to obtain the preference vector of the final user for the item. The MAE and RMSE of the recommended results are employed as the experimenalevaluation indicators on four public data sets. The experimental results show that the proposed model has better recommendation effect and running time compared with the existing seven typical recommendation models.

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钱忠胜,赵畅,俞情媛,李端明.结合注意力CNN与GNN的信息融合推荐方法.软件学报,2023,34(5):2317-2336

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历史
  • 收稿日期:2020-08-17
  • 最后修改日期:2020-12-13
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  • 在线发布日期: 2022-07-07
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