{"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/crfr-improving-conversational-recommender","title":"CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs","arxiv_id":null,"date":"2021-11-01","proceeding":"EMNLP 2021 11","authors":["Jinfeng Zhou","Bo wang","Ruifang He","Yuexian Hou"],"abstract":"Although paths of user interests shift in knowledge graphs (KGs) can benefit conversational recommender systems (CRS), explicit reasoning on KGs has not been well considered in CRS, due to the complex of high-order and incomplete paths. We propose CRFR, which effectively does explicit multi-hop reasoning on KGs with a conversational context-based reinforcement learning model. Considering the incompleteness of KGs, instead of learning single complete reasoning path, CRFR flexibly learns multiple reasoning fragments which are likely contained in the complete paths of interests shift. A fragments-aware unified model is then designed to fuse the fragments information from item-oriented and concept-oriented KGs to enhance the CRS response with entities and words from the fragments. Extensive experiments demonstrate CRFR’s SOTA performance on recommendation, conversation and conversation interpretability.","url_abs":"https://aclanthology.org/2021.emnlp-main.355","url_pdf":"https://aclanthology.org/2021.emnlp-main.355.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":[],"tasks":[{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/recommendation-systems-on-redial","task":"Recommendation Systems","dataset":"ReDial","model":"CRFR","rank_in_archive_order":4,"of":8,"metrics":{"Recall@1":"0.04","Recall@10":"0.202","Recall@50":"0.399"},"uses_additional_data":false},{"leaderboard":"/sota/text-generation-on-redial","task":"Text Generation","dataset":"ReDial","model":"CRFR","rank_in_archive_order":2,"of":5,"metrics":{"Distinct-2":"0.345","Distinct-3":"0.516","Distinct-4":"0.639"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}