l -Injection: Toward Effective Collaborative Filtering Using Uninteresting Items
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jongwuk | - |
dc.contributor.author | Hwang, Won-Seok | - |
dc.contributor.author | Parc, Juan | - |
dc.contributor.author | Lee, Youngnam | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.contributor.author | Lee, Dongwon | - |
dc.date.accessioned | 2022-07-10T14:54:01Z | - |
dc.date.available | 2022-07-10T14:54:01Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2019-01 | - |
dc.identifier.issn | 1041-4347 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/148481 | - |
dc.description.abstract | We develop a novel framework, named as iota-injection, to address the sparsity problem of recommender systems. By carefully injecting low values to a selected set of unrated user-item pairs in a user-item matrix, we demonstrate that top-N recommendation accuracies of various collaborative filtering (CF) techniques can be significantly and consistently improved. We first adopt the notion of pre-use preferences of users toward a vast amount of unrated items. Using this notion, we identify uninteresting items that have not been rated yet but are likely to receive low ratings from users, and selectively impute them as low values. As our proposed approach is method-agnostic, it can be easily applied to a variety of CF algorithms. Through comprehensive experiments with three real-life datasets (e.g., Movielens, Ciao, and Watcha), we demonstrate that our solution consistently and universally enhances the accuracies of existing CF algorithms (e.g., item-based CF, SVD-based CF, and SVD++) by 2.5 to 5 times on average. Furthermore, our solution improves the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | l -Injection: Toward Effective Collaborative Filtering Using Uninteresting Items | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1109/TKDE.2017.2698461 | - |
dc.identifier.scopusid | 2-s2.0-85058210716 | - |
dc.identifier.wosid | 000452519800002 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.31, no.1, pp.3 - 16 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.citation.title | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.citation.volume | 31 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 3 | - |
dc.citation.endPage | 16 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | Passive filters | - |
dc.subject.keywordPlus | Recommender systems | - |
dc.subject.keywordPlus | Data sparsity | - |
dc.subject.keywordPlus | post-use preference | - |
dc.subject.keywordPlus | pre-use preference | - |
dc.subject.keywordPlus | Real life datasets | - |
dc.subject.keywordPlus | Recommendation accuracy | - |
dc.subject.keywordPlus | Sparsity problems | - |
dc.subject.keywordPlus | uninteresting items | - |
dc.subject.keywordPlus | User-item matrix | - |
dc.subject.keywordPlus | Collaborative filtering | - |
dc.subject.keywordAuthor | Recommender systems | - |
dc.subject.keywordAuthor | collaborative filtering | - |
dc.subject.keywordAuthor | data sparsity | - |
dc.subject.keywordAuthor | uninteresting items | - |
dc.subject.keywordAuthor | pre-use preference | - |
dc.subject.keywordAuthor | post-use preference | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/7913668 | - |
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