{"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/explainable-reasoning-over-knowledge-graphs","title":"Explainable Reasoning over Knowledge Graphs for Recommendation","arxiv_id":"1811.04540","date":"2018-11-12","proceeding":null,"authors":["Xiang Wang","Dingxian Wang","Canran Xu","Xiangnan He","Yixin Cao","Tat-Seng Chua"],"abstract":"Incorporating knowledge graph into recommender systems has attracted\nincreasing attention in recent years. By exploring the interlinks within a\nknowledge graph, the connectivity between users and items can be discovered as\npaths, which provide rich and complementary information to user-item\ninteractions. Such connectivity not only reveals the semantics of entities and\nrelations, but also helps to comprehend a user's interest. However, existing\nefforts have not fully explored this connectivity to infer user preferences,\nespecially in terms of modeling the sequential dependencies within and holistic\nsemantics of a path. In this paper, we contribute a new model named\nKnowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for\nrecommendation. KPRN can generate path representations by composing the\nsemantics of both entities and relations. By leveraging the sequential\ndependencies within a path, we allow effective reasoning on paths to infer the\nunderlying rationale of a user-item interaction. Furthermore, we design a new\nweighted pooling operation to discriminate the strengths of different paths in\nconnecting a user with an item, endowing our model with a certain level of\nexplainability. We conduct extensive experiments on two datasets about movie\nand music, demonstrating significant improvements over state-of-the-art\nsolutions Collaborative Knowledge Base Embedding and Neural Factorization\nMachine.","url_abs":"http://arxiv.org/abs/1811.04540v1","url_pdf":"http://arxiv.org/pdf/1811.04540v1.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":"explainable-reasoning-over-knowledge-graphs","repo_url":"https://github.com/eBay/KPRN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"explainable-reasoning-over-knowledge-graphs","repo_url":"https://github.com/terwilligers/knowledge-graph-recommender","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}