{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:02:43Z","timestamp":1772121763013,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Social recommendation aims to improve the recommendation performance by learning user interest and social representations from users\u2019 interaction records and social relations. Intuitively, these learned representations entangle user interest factors with social factors because users\u2019 interaction behaviors and social relations affect each other. A high-quality recommender system should provide items to a user according to his\/her interest factors. However, most existing social recommendation models aggregate the two kinds of representations indiscriminately, and this kind of aggregation limits their recommendation performance. In this paper, we develop a model called <jats:bold>D<\/jats:bold>isentangled <jats:bold>V<\/jats:bold>ariational autoencoder for <jats:bold>S<\/jats:bold>ocial <jats:bold>R<\/jats:bold>ecommendation (DVSR) to disentangle interest and social factors from the two kinds of user representations. Firstly, we perform a preliminary analysis of the entangled information on three popular social recommendation datasets. Then, we present the model architecture of DVSR, which is based on the Variational AutoEncoder (VAE) framework. Besides the traditional method of training VAE, we also use contrastive estimation to penalize the mutual information between interest and social factors. Extensive experiments are conducted on three benchmark datasets to evaluate the effectiveness of our model.<\/jats:p>","DOI":"10.1007\/s11063-024-11607-y","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T18:02:11Z","timestamp":1714413731000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Disentangled Variational Autoencoder for Social Recommendation"],"prefix":"10.1007","volume":"56","author":[{"given":"Yongshuai","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"11607_CR1","unstructured":"van\u00a0den Berg R, Kipf TN, Welling M (2017) Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263"},{"key":"11607_CR2","doi-asserted-by":"crossref","unstructured":"Ma H, Yang H, Lyu MR, King I (2008) SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM 2008), (pp. 931\u2013940)","DOI":"10.1145\/1458082.1458205"},{"issue":"5","key":"11607_CR3","doi-asserted-by":"publisher","first-page":"5357","DOI":"10.1007\/s11063-022-10917-3","volume":"55","author":"A Sattar","year":"2023","unstructured":"Sattar A, Bacciu D (2023) Graph neural network for context-aware recommendation. Neural Process Lett 55(5):5357\u20135376","journal-title":"Neural Process Lett"},{"issue":"4","key":"11607_CR4","doi-asserted-by":"publisher","first-page":"5013","DOI":"10.1007\/s11063-022-11077-0","volume":"55","author":"C Wang","year":"2023","unstructured":"Wang C, Zhang H, Li L, Li D (2023) Knowledge graph attention network with attribute significance for personalized recommendation. Neural Process Lett 55(4):5013\u20135029","journal-title":"Neural Process Lett"},{"key":"11607_CR5","doi-asserted-by":"crossref","unstructured":"Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the 4th International Conference on Web Search and Web Data Mining (WSDM 2011), (pp. 287\u2013296)","DOI":"10.1145\/1935826.1935877"},{"key":"11607_CR6","doi-asserted-by":"crossref","unstructured":"Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 2010 ACM Conference on Recommender Systems (RecSys 2010), (pp. 135\u2013142)","DOI":"10.1145\/1864708.1864736"},{"key":"11607_CR7","unstructured":"Yang B, Lei Y, Liu D, Liu J (2013) Social collaborative filtering by trust. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), (pp. 2747\u20132753)"},{"issue":"8","key":"11607_CR8","doi-asserted-by":"publisher","first-page":"3727","DOI":"10.1109\/TKDE.2020.3033673","volume":"34","author":"J Yu","year":"2022","unstructured":"Yu J, Yin H, Li J, Gao M, Huang Z, Cui L (2022) Enhance social recommendation with adversarial graph convolutional networks. IEEE Trans Knowl Data Eng 34(8):3727\u20133739","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11607_CR9","doi-asserted-by":"crossref","unstructured":"Yu J, Gao M, Li J, Yin H, Liu H (2018) Adaptive implicit friends identification over heterogeneous network for social recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), (pp. 357\u2013366)","DOI":"10.1145\/3269206.3271725"},{"key":"11607_CR10","doi-asserted-by":"crossref","unstructured":"Yang L, Liu Z, Dou Y, Ma J, Yu PS (2021) ConsisRec: enhancing GNN for social recommendation via consistent neighbor aggregation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), (pp. 2141\u20132145)","DOI":"10.1145\/3404835.3463028"},{"issue":"2","key":"11607_CR11","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1007\/s11063-018-9831-7","volume":"49","author":"M Wang","year":"2019","unstructured":"Wang M, Wu Z, Sun X, Feng G, Zhang B (2019) Trust-aware collaborative filtering with a denoising autoencoder. Neural Process Lett 49(2):835\u2013849","journal-title":"Neural Process Lett"},{"key":"11607_CR12","doi-asserted-by":"crossref","unstructured":"Fan W, Derr T, Ma Y, Wang J, Tang J, Li Q (2019) Deep adversarial social recommendation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), (pp. 1351\u20131357)","DOI":"10.24963\/ijcai.2019\/187"},{"key":"11607_CR13","doi-asserted-by":"crossref","unstructured":"Yu J, Yin H, Gao M, Xia X, Zhang X, Hung NQV (2021) Socially-aware self-supervised tri-training for recommendation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), (pp. 2084\u20132092)","DOI":"10.1145\/3447548.3467340"},{"key":"11607_CR14","unstructured":"Kim H, Mnih A (2018) Disentangling by factorising. In: Proceedings of the 35th International Conference on Machine Learning (ICML 2018), (pp. 2654\u20132663)"},{"key":"11607_CR15","doi-asserted-by":"crossref","unstructured":"Ma L, Sun Q, Georgoulis S, Gool LV, Schiele B, Fritz M (2018) Disentangled person image generation. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), (pp. 99\u2013108)","DOI":"10.1109\/CVPR.2018.00018"},{"key":"11607_CR16","unstructured":"Higgins I, Matthey L, Pal A, Burgess CP, Glorot X, Botvinick MM, Mohamed S, Lerchner A (2017) beta-VAE: learning basic visual concepts with a constrained variational framework. In: The 5th International Conference on Learning Representations (ICLR 2017)"},{"key":"11607_CR17","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational Bayes. In: The 2nd International Conference on Learning Representations (ICLR 2014)"},{"key":"11607_CR18","doi-asserted-by":"crossref","unstructured":"Zhang K, Liu Q, Huang Z, Cheng M, Zhang K, Zhang M, Wu W, Chen E (2022) Graph adaptive semantic transfer for cross-domain sentiment classification. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), (pp. 1566\u20131576)","DOI":"10.1145\/3477495.3531984"},{"key":"11607_CR19","doi-asserted-by":"crossref","unstructured":"Tian J, Wang K, Xu X, Cao Z, Shen F, Shen HT (2022) Multimodal disentanglement variational autoencoders for zero-shot cross-modal retrieval. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), (pp. 960\u2013969)","DOI":"10.1145\/3477495.3532028"},{"key":"11607_CR20","doi-asserted-by":"crossref","unstructured":"Fan W, Ma Y, Li Q, He Y, Zhao YE, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: Proceedings of the 2019 World Wide Web Conference (WWW 2019), (pp. 417\u2013426)","DOI":"10.1145\/3308558.3313488"},{"issue":"1","key":"11607_CR21","first-page":"464","volume":"51","author":"L Wu","year":"2021","unstructured":"Wu L, Sun P, Hong R, Ge Y, Wang M (2021) Collaborative neural social recommendation. IEEE Trans Knowl Data Eng 51(1):464\u2013476","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"10","key":"11607_CR22","doi-asserted-by":"publisher","first-page":"4753","DOI":"10.1109\/TKDE.2020.3048414","volume":"34","author":"L Wu","year":"2022","unstructured":"Wu L, Li J, Sun P, Hong R, Ge Y, Wang M (2022) DiffNet++: a neural influence and interest diffusion network for social recommendation. IEEE Trans Knowl Data Eng 34(10):4753\u20134766","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"11607_CR23","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1007\/s11063-021-10475-0","volume":"53","author":"S Abinaya","year":"2021","unstructured":"Abinaya S, Devi MKK (2021) Enhancing top-N recommendation using stacked autoencoder in context-aware recommender system. Neural Process Lett 53(3):1865\u20131888","journal-title":"Neural Process Lett"},{"key":"11607_CR24","doi-asserted-by":"crossref","unstructured":"Sedhain S, Menon AK, Sanner S, Xie L (2015) AutoRec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web (WWW 2015), (pp. 111\u2013112)","DOI":"10.1145\/2740908.2742726"},{"key":"11607_CR25","doi-asserted-by":"crossref","unstructured":"Liang D, Krishnan RG, Hoffman MD, Jebara T (2018) Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference (WWW 2018), (pp. 689\u2013698)","DOI":"10.1145\/3178876.3186150"},{"key":"11607_CR26","doi-asserted-by":"crossref","unstructured":"Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-N recommender systems. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM 2016), (pp. 153\u2013162)","DOI":"10.1145\/2835776.2835837"},{"key":"11607_CR27","doi-asserted-by":"crossref","unstructured":"Lee W, Song K, Moon I (2017) Augmented variational autoencoders for collaborative filtering with auxiliary information. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM 2017), (pp. 153\u2013162)","DOI":"10.1145\/3132847.3132972"},{"key":"11607_CR28","doi-asserted-by":"crossref","unstructured":"Liu D, Cheng P, Zhu H, Dong Z, He X, Pan W, Ming Z (2021) Mitigating confounding bias in recommendation via information bottleneck. In: Proceedings of the 15th ACM Conference on Recommender Systems (RecSys 2021), (pp. 351\u2013360)","DOI":"10.1145\/3460231.3474263"},{"issue":"10","key":"11607_CR29","doi-asserted-by":"publisher","first-page":"9920","DOI":"10.1109\/TKDE.2022.3218994","volume":"35","author":"Z Zhao","year":"2023","unstructured":"Zhao Z, Chen J, Zhou S, He X, Cao X, Zhang F, Wu W (2023) Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation. IEEE Trans Knowl Data Eng 35(10):9920\u20139931","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11607_CR30","doi-asserted-by":"crossref","unstructured":"Nema P, Karatzoglou A, Radlinski F (2021) Disentangling preference representations for recommendation critiquing with \u00df-VAE. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), (pp. 1356\u20131365)","DOI":"10.1145\/3459637.3482425"},{"key":"11607_CR31","unstructured":"Ma J, Zhou C, Cui P, Yang H, Zhu W (2019) Learning disentangled representations for recommendation. In: Advances in Neural Information Processing Systems 32 (NeurIPS 2019), (pp. 5712\u20135723)"},{"key":"11607_CR32","doi-asserted-by":"crossref","unstructured":"Wu J, Fan W, Chen J, Liu S, Li Q, Tang K (2022) Disentangled contrastive learning for social recommendation. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), (pp. 4570\u20134574)","DOI":"10.1145\/3511808.3557583"},{"issue":"1","key":"11607_CR33","first-page":"61","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Knowl Data Eng 20(1):61\u201380","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11607_CR34","doi-asserted-by":"crossref","unstructured":"Sun Y, Sun Z, Sha X, Zhang J, Ong YS (2023) Disentangling motives behind item consumption and social connection for mutually-enhanced joint prediction. In: Proceedings of the 17th ACM Conference on Recommender Systems, (RecSys 2023), (pp. 613\u2013624)","DOI":"10.1145\/3604915.3608767"},{"issue":"3","key":"11607_CR35","first-page":"867","volume":"9","author":"X Sha","year":"2022","unstructured":"Sha X, Sun Z, Zhang J (2022) Disentangling multi-facet social relations for recommendation. IEEE Trans Knowl Data Eng 9(3):867\u2013878","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"11607_CR36","first-page":"5738","volume":"35","author":"N Li","year":"2023","unstructured":"Li N, Gao C, Jin D, Liao Q (2023) Disentangled modeling of social homophily and influence for social recommendation. IEEE Trans Knowl Data Eng 35(6):5738\u20135751","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11607_CR37","unstructured":"Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative models. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014), (pp. 1278\u20131286)"},{"issue":"10","key":"11607_CR38","doi-asserted-by":"publisher","first-page":"1854","DOI":"10.1109\/TKDE.2019.2913394","volume":"32","author":"L Wu","year":"2020","unstructured":"Wu L, Chen L, Hong R, Fu Y, Xie X, Wang M (2020) A hierarchical attention model for social contextual image recommendation. IEEE Trans Knowl Data Eng 32(10):1854\u20131867","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11607_CR39","doi-asserted-by":"crossref","unstructured":"Tang J, Gao H, Liu H (2012) mTrust: Discerning multi-faceted trust in a connected world. In: Proceedings of the 5th International Conference on Web Search and Web Data Mining (WSDM 2012), pp. 93\u2013102","DOI":"10.1145\/2124295.2124309"},{"key":"11607_CR40","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M, Feng F, Chua T (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), (pp. 165\u2013174)","DOI":"10.1145\/3331184.3331267"},{"key":"11607_CR41","unstructured":"Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: The 3rd International Conference on Learning Representations (ICLR 2015)"},{"key":"11607_CR42","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) LightGCN: Simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), (pp. 639\u2013648)","DOI":"10.1145\/3397271.3401063"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11607-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-024-11607-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11607-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T11:14:43Z","timestamp":1721042083000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-024-11607-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,29]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["11607"],"URL":"https:\/\/doi.org\/10.1007\/s11063-024-11607-y","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,29]]},"assertion":[{"value":"21 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"159"}}