A Tripartite-Graph Based Recommendation Framework for Price-Comparison Services
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sang-Chul | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.contributor.author | Park, Sunju | - |
dc.contributor.author | Chae, Dong-Kyu | - |
dc.date.accessioned | 2022-07-09T14:27:36Z | - |
dc.date.available | 2022-07-09T14:27:36Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2019-06 | - |
dc.identifier.issn | 1820-0214 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147691 | - |
dc.description.abstract | 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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | COMSIS CONSORTIUM | - |
dc.title | A Tripartite-Graph Based Recommendation Framework for Price-Comparison Services | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.contributor.affiliatedAuthor | Chae, Dong-Kyu | - |
dc.identifier.doi | 10.2298/CSIS181012005L | - |
dc.identifier.scopusid | 2-s2.0-85073288319 | - |
dc.identifier.wosid | 000474377300001 | - |
dc.identifier.bibliographicCitation | COMPUTER SCIENCE AND INFORMATION SYSTEMS, v.16, no.2, pp.333 - 357 | - |
dc.relation.isPartOf | COMPUTER SCIENCE AND INFORMATION SYSTEMS | - |
dc.citation.title | COMPUTER SCIENCE AND INFORMATION SYSTEMS | - |
dc.citation.volume | 16 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 333 | - |
dc.citation.endPage | 357 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordPlus | RANDOM-WALK | - |
dc.subject.keywordPlus | SIMILARITY | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | recommendation systems | - |
dc.subject.keywordAuthor | price-comparison services | - |
dc.subject.keywordAuthor | random walk with restart | - |
dc.identifier.url | https://doiserbia.nb.rs/Article.aspx?ID=1820-02141900005L | - |
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