{"type":"ScholarlyArticle","title":"Recommendation Algorithms for Optimizing Hit Rate, User Satisfaction and Website Revenue","authors":[{"name":"Xin Wang","slug":"xin-wang-2","given":"Xin","family":"Wang","affiliation":""},{"name":"Yunhui Guo","slug":"yunhui-guo","given":"Yunhui","family":"Guo","affiliation":""},{"name":"Congfu Xu","slug":"congfu-xu","given":"Congfu","family":"Xu","affiliation":""}],"abstract":"We generally use hit rate to measure the performance of item recommendation algorithms. In addition to hit rate, we consider another two important factors which are ignored by most previous works. First, whether users are satisfied with the recommended items. It is possible that a user has bought an item but dislikes it. Hence high hit rate does not reflect high customer satisfaction. Second, whether the website retailers are satisfied with the recommendation results. If a customer is interested in two products and wants to buy one of them, it may be better to suggest the item which can help bring more profit. Therefore, a good recommendation algorithm should not only consider improving hit rate but also consider optimizing user satisfaction and website revenue. In this paper, we propose two algorithms for the above purposes and design two modified hit rate based metrics to measure them. Experimental results on 10 real-world datasets show that our methods can not only achieve better hit rate, but improve user satisfaction and website revenue comparing with the state-of-the-art models.","doi":null,"url":"https://ijcai.org/proceedings/2015/259","pdf":"https://ijcai.org/proceedings/2015/0259.pdf","markdown":"https://ijcai.org/proceedings/2015/259.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/259","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Recommender Systems","track":"Main Track","number":259,"pages":{"first":1820,"last":1825},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}