Social Recommendation Algorithm Based on Attention Gated Neural Network

doi:10.3778/j.issn.1002-8331.2101-0516

Abstract

Abstract: To address the problem of low recommendation accuracy in social recommendation algorithms, a multi-headed attention gating neural network（MAGN） algorithm is proposed. Specifically, a gated neural network is used to fuse the input user and the user’s friend pairs to obtain a joint embedding, then an attentional memory network is used to obtain the influence of different friends on the user in different aspects, and then multi-headed attention is used to obtain several friends who have a high degree of influence on the user in different aspects. Finally, a gated neural network is used to mix friend influence with the user’s own interest preferences, and items are then recommended based on mixed interest preferences. Experiments on two publicly real available datasets further validate the effectiveness of the proposed method.

Key words: recommendation system, deep learning, attention mechanism, gated neural network

摘要： 针对社会化推荐算法中存在的推荐准确率不高的问题，提出了一种多头注意力门控神经网络（MAGN）算法。具体来说，采用门控神经网络对输入的用户和用户-朋友对进行融合得到联合嵌入，利用注意力记忆网络来获取不同朋友在不同方面对用户的影响，利用多头注意力来获取在不同方面对用户影响程度偏高的几位朋友。采用门控神经网络将朋友影响和用户自身兴趣偏好进行混合，继而基于混合兴趣偏好对项目进行推荐。在两个公开真实数据集上进行实验进一步验证了所提方法的有效性。

关键词: 推荐系统, 深度学习, 注意力机制, 门控神经网络

QIU Ye, SHAO Xiongkai, GAO Rong, WANG Chunzhi, LI Jing. Social Recommendation Algorithm Based on Attention Gated Neural Network[J]. Computer Engineering and Applications, 2022, 58(5): 112-118.

邱叶, 邵雄凯, 高榕, 王春枝, 李晶. 基于注意力门控神经网络的社会化推荐算法[J]. 计算机工程与应用, 2022, 58(5): 112-118.

References

[1] 李浩，张亚钏，康雁，等.融合循环知识图谱和协同过滤电影推荐算法[J].计算机工程与应用，2020，56（2）：106-114.
LI H，ZHANG Y C，KANG Y，et al.Fusion recurrent knowledge graph and collaborative filtering movie recommendation algorithm[J].Computer Engineering and Applications，2020，56（2）：106-114.
[2] BO Y，YU L，LIU J M，et al.Social collaborative filtering by trust[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，39（8）：1633-1647.
[3] CHEN X，ZHANG Y F，QIN Z.Dynamic explainable recommendation based on neural attentive models[C]//Proceedings of the AAAI Conference on Artificial Intelligence，Hawaii，Jan 27-Feb 1，2019.Menlo Park：AAAI，2019：53-60.
[4] LIU D H，LI J，DU B，et al.DAML：dual attention mutual learning between ratings and reviews for item recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，Alaska，Aug 4-8，2019.New York：ACM，2019：344-352.
[5] YU J L，GAO M，LI J D，et al.Adaptive implicit friends identification over heterogeneous network for social recommendation[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management，Turin，Oct 22-26，2018.New York：ACM，2018：357-366.
[6] JIN J，QIN J，FANG Y C，et al.An efficient neighborhood-based interaction model for recommendation on heterogeneous graph[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York：ACM，2020：75-84.
[7] CHEN C，ZHANG M，LIU Y Q，et al.Social attentional memory network：modeling aspect- and friend-level differences in recommendation[C]//Proceedings of the 12th ACM International Conference on Web Search and Data Mining，Melbourne，Feb 11-15，2019.New York：ACM，2019：177-185.
[8] VASWANI A，SHAZEER N，PARMAR N，et al.Attention is all you need[C]//Advances in Neural Information Processing Systems，Long Beach，CA，Dec 4-9，2017.New York：Curran Associates，Inc，2017：5998-6008.
[9] CHENG Z Y，DING Y，HE X N，et al.A^3NCF：an adaptive aspect attention model for rating prediction[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence，Louisiana，Feb 2-7，2018.Menlo Park：AAAI，2018：3748-3754.
[10] PEI W J，YANG J，SUN Z，et al.Interacting attention-gated recurrent networks for recommendation[C]//Proceedings of the 2017 ACM Conference on Information and Knowledge Management，Singapore，Nov 6-10，2017.New York：ACM，2017：1459-1468.
[11] 柴玉梅，员武莲，王黎明，等.基于双注意力机制和迁移学习的跨领域推荐模型[J].计算机学报，2020，43（10）：1924-1942.
CHAI Y M，YUAN W L，WANG L M，et al.A cross-domain recommendation model based on dual attention mechanism and transfer learning[J].Chinese Journal of Computers，2020，43（10）：1924-1942.
[12] JI M，JOO W，SONG K，et al.Sequential recommendation with relation-aware kernelized self-attention[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Menlo Park：AAAI，2020：4304-4311.
[13] TAY Y，LUU T，HUI S C.Latent relational metric learning via memory-based attention for collaborative ranking[C]//Proceedings of the 2018 World Wide Web Conference，Lyon，Apr 23-27，2018.Berlin：Springer，2018：729-739.
[14] LU Y F，XIE R B，SHI C，et al.Social influence attentive neural network for friend-enhanced recommendation[C]//Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases，Belgium，2020.
[15] 吴宾，娄铮铮，叶阳东.联合正则化的矩阵分解推荐算法[J].软件学报，2018，29（9）：2681-2696.
WU B，LOU Z Z，YE Y D.Co-regularized matrix factorization recommendation algorithm[J].Journal of Software，2018，29（9）：2681-2696.
[16] MA C，KANG P，WU B，et al.Gated attentive-autoencoder for content-aware recommendation[C]//Proceedings of the 12th ACM International Conference on Web Search and Data Mining，Melbourne，Feb 11-15，2019.New York：ACM，2019：519-527.
[17] XIA H F，WANG Z M，DU B，et al.Leveraging ratings and reviews with gating mechanism for recommendation[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management，Beijing，Nov 3-7，2019.New York：ACM，2019：1573-1582.
[18] MA C，MA L H，ZHANG Y X，et al.Memory augmented graph neural networks for sequential recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Menlo Park：AAAI，2020：5045-5052.
[19] DUCHI J，HAZAN E，SINGER Y.Adaptive subgradient methods for online learning and stochastic optimization[J].Journal of Machine Learning Research，2011，12（61）：2121-2159.
[20] CANTADOR I，PETER B，TSVI K.Second workshop on information heterogeneity and fusion in recommender systems[C]//Proceedings of the 5th ACM Conference on Recommender Systems，Chicago，Oct 23-27，2011.New York：ACM，2011：387-388.
[21] MASSA P，KASPER S，MARTINO S，et al.Trustlet，open research on trust metrics[J].Scalable Computing：Practice and Experience，2008，9（4）：31-44.
[22] HE X N，CHUA T S.Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval，Shinjuku，Aug 7-11，2017.New York：ACM，2017：355-364.
[23] HE X N，LIAO L Z，ZHANG H W，et al.Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web，Perth，Apr 3-7，2017.Berlin，German：Springer，2017：173-182.