{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-neural-influence-diffusion-model-for-social","title":"A Neural Influence Diffusion Model for Social Recommendation","arxiv_id":"1904.10322","date":"2019-04-20","proceeding":null,"authors":["Le Wu","Peijie Sun","Yanjie Fu","Richang Hong","Xiting Wang","Meng Wang"],"abstract":"Precise user and item embedding learning is the key to building a successful\nrecommender system. Traditionally, Collaborative Filtering(CF) provides a way\nto learn user and item embeddings from the user-item interaction history.\nHowever, the performance is limited due to the sparseness of user behavior\ndata. With the emergence of online social networks, social recommender systems\nhave been proposed to utilize each user's local neighbors' preferences to\nalleviate the data sparsity for better user embedding modeling. We argue that,\nfor each user of a social platform, her potential embedding is influenced by\nher trusted users. As social influence recursively propagates and diffuses in\nthe social network, each user's interests change in the recursive process.\nNevertheless, the current social recommendation models simply developed static\nmodels by leveraging the local neighbors of each user without simulating the\nrecursive diffusion in the global social network, leading to suboptimal\nrecommendation performance. In this paper, we propose a deep influence\npropagation model to stimulate how users are influenced by the recursive social\ndiffusion process for social recommendation. For each user, the diffusion\nprocess starts with an initial embedding that fuses the related features and a\nfree user latent vector that captures the latent behavior preference. The key\nidea of our proposed model is that we design a layer-wise influence propagation\nstructure to model how users' latent embeddings evolve as the social diffusion\nprocess continues. We further show that our proposed model is general and could\nbe applied when the user~(item) attributes or the social network structure is\nnot available. Finally, extensive experimental results on two real-world\ndatasets clearly show the effectiveness of our proposed model, with more than\n13% performance improvements over the best baselines.","url_abs":"http://arxiv.org/abs/1904.10322v1","url_pdf":"http://arxiv.org/pdf/1904.10322v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-neural-influence-diffusion-model-for-social","repo_url":"https://github.com/PeiJieSun/diffnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-neural-influence-diffusion-model-for-social","repo_url":"https://github.com/Kanika91/diffnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.10322","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}