{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T10:33:07Z","timestamp":1722249187665},"reference-count":0,"publisher":"National Library of Serbia","issue":"2","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2019]]},"abstract":"<jats:p>The recommender systems help users who are going through numerous items (e.g., movies or music) presented in online shops by capturing each user?s preferences on items and suggesting a set of personalized items that s\/he is likely to prefer [8]. They have been extensively studied in the academic society and widely utilized in many online shops [33]. However, to the best of our knowledge, recommending items to users in price-comparison services has not been studied extensively yet, which could attract a great deal of attention from shoppers these days due to its capability to save users? time who want to purchase items with the lowest price [31]. In this paper, we examine why existing recommendation methods cannot be directly applied to price-comparison services, and propose three recommendation strategies that are tailored to price-comparison services: (1) using click-log data to identify users? preferences, (2) grouping similar items together as a user?s area of interest, and (3) exploiting the category hierarchy and keyword information of items. We implement these strategies into a unified recommendation framework based on a tripartite graph. Through our extensive experiments using real-world data obtained from Naver shopping, one of the largest price-comparison services in Korea, the proposed framework improved recommendation accuracy up to 87% in terms of precision and 129% in terms of recall, compared to the most competitive baseline.<\/jats:p>","DOI":"10.2298\/csis181012005l","type":"journal-article","created":{"date-parts":[[2019,6,11]],"date-time":"2019-06-11T11:48:27Z","timestamp":1560253707000},"page":"333-357","source":"Crossref","is-referenced-by-count":2,"title":["A tripartite-graph based recommendation framework for price-comparison services"],"prefix":"10.2298","volume":"16","author":[{"given":"Sang-Chul","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Computer and Software Hanyang University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sang-Wook","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer and Software Hanyang University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunju","family":"Park","sequence":"additional","affiliation":[{"name":"School of Business Yonsei University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong-Kyu","family":"Chae","sequence":"additional","affiliation":[{"name":"Department of Computer and Software Hanyang University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:33:45Z","timestamp":1685349225000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02141900005L"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019]]}},"URL":"https:\/\/doi.org\/10.2298\/csis181012005l","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}