{"url":"/method/comirec","slug":"comirec","name":"ComiRec","full_name":"ComiRec","full_name_withheld":false,"description_markdown":"**ComiRec** is a multi-interest framework for sequential recommendation. The multi-interest module captures multiple interests from user behavior sequences, which can be exploited for retrieving candidate items from the large-scale item pool. These items are then fed into an aggregation module to obtain the overall recommendation. The aggregation module leverages a controllable factor to balance the recommendation accuracy and diversity.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Controllable Multi-Interest Framework for Recommendation","paper":"/paper/controllable-multi-interest-framework-for","first_author":"Yukuo Cen","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/controllable-multi-interest-framework-for"},"source":{"url":"https://arxiv.org/abs/2005.09347v2","title":"Controllable Multi-Interest Framework for Recommendation","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Recommendation Systems","url":"/methods/category/recommendation-systems","pwc_aliases":[]}],"n_papers_tagged":3,"archive_num_papers":3,"papers_newest_first":[{"paper":"/paper/re4-learning-to-re-contrast-re-attend-re","title":"Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation","date":"2022-08-17","arxiv_id":"2208.08011","n_code_links":1,"syntology":null},{"paper":"/paper/when-multi-level-meets-multi-interest-a-multi","title":"When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation","date":"2022-05-03","arxiv_id":"2205.01286","n_code_links":1,"syntology":{"ran":0,"of":6,"unverified":6,"pointer_only":0}},{"paper":"/paper/controllable-multi-interest-framework-for","title":"Controllable Multi-Interest Framework for Recommendation","date":"2020-05-19","arxiv_id":"2005.09347","n_code_links":2,"syntology":{"ran":0,"of":9,"unverified":9,"pointer_only":0}}],"papers_shown":3,"tasks":[{"task":"/task/recommendation-systems","name":"Recommendation Systems","papers":2},{"task":"/task/sequential-recommendation","name":"Sequential Recommendation","papers":2},{"task":"/task/contrastive-learning","name":"Contrastive Learning","papers":1},{"task":"/task/diversity","name":"Diversity","papers":1}],"tasks_shown":4,"n_tasks":4,"usage_by_year":[{"year":"2020","papers":1},{"year":"2022","papers":2}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/comirec"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}