{"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/hybrid-recommender-systems-a-systematic","title":"Hybrid Recommender Systems: A Systematic Literature Review","arxiv_id":"1901.03888","date":"2019-01-12","proceeding":null,"authors":["Erion Çano","Maurizio Morisio"],"abstract":"Recommender systems are software tools used to generate and provide\nsuggestions for items and other entities to the users by exploiting various\nstrategies. Hybrid recommender systems combine two or more recommendation\nstrategies in different ways to benefit from their complementary advantages.\nThis systematic literature review presents the state of the art in hybrid\nrecommender systems of the last decade. It is the first quantitative review\nwork completely focused in hybrid recommenders. We address the most relevant\nproblems considered and present the associated data mining and recommendation\ntechniques used to overcome them. We also explore the hybridization classes\neach hybrid recommender belongs to, the application domains, the evaluation\nprocess and proposed future research directions. Based on our findings, most of\nthe studies combine collaborative filtering with another technique often in a\nweighted way. Also cold-start and data sparsity are the two traditional and top\nproblems being addressed in 23 and 22 studies each, while movies and movie\ndatasets are still widely used by most of the authors. As most of the studies\nare evaluated by comparisons with similar methods using accuracy metrics,\nproviding more credible and user oriented evaluations remains a typical\nchallenge. Besides this, newer challenges were also identified such as\nresponding to the variation of user context, evolving user tastes or providing\ncross-domain recommendations. Being a hot topic, hybrid recommenders represent\na good basis with which to respond accordingly by exploring newer opportunities\nsuch as contextualizing recommendations, involving parallel hybrid algorithms,\nprocessing larger datasets, etc.","url_abs":"http://arxiv.org/abs/1901.03888v1","url_pdf":"http://arxiv.org/pdf/1901.03888v1.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":"hybrid-recommender-systems-a-systematic","repo_url":"https://github.com/rashmi1112/Reccomendation-System-for-E-Commerce-Application","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"systematic-literature-review","task_name":"Systematic Literature Review"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}