{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T15:22:55Z","timestamp":1780413775443,"version":"3.54.1"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"Autumn","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGWEB Newsl."],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:p>\n            Recommender systems aim to predict user preferences by analyzing users' past behavior. Collaborative filtering (CF) is one of the key techniques employed in recommender systems that uses explicit (\n            <jats:italic>e.g.<\/jats:italic>\n            , ratings) and implicit (\n            <jats:italic>e.g.<\/jats:italic>\n            , browsing) feedback from users to predict unknown feedback, providing top-\n            <jats:italic>N<\/jats:italic>\n            recommendations. However, CF faces challenges when dealing with sparse data, which can decrease the accuracy of recommendations. To overcome these inherent challenges in recommender systems, this article introduces the concept of \"uninteresting items\" that have not been rated by a user, but are unlikely to be liked even when recommended. We then review our previous works that utilize both positive preferences from rated items and negative preferences from uninteresting items to improve recommendation accuracy. Specifically, we discuss a family of our eight CF methods that are assisted by the uninteresting items: Zero-injection (ZI),\n            <jats:italic>l<\/jats:italic>\n            -injection, Imputation, RAGAN, and Deep-ZI, which are designed for explicit feedback, as well as gOCCF, M-BPR, and CNS, which are designed for implicit feedback. Also, we report some evaluation results for showing their effectiveness.\n          <\/jats:p>","DOI":"10.1145\/3631358.3631362","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T17:24:20Z","timestamp":1702315460000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Uninteresting Items: Concept and Its Application to Effective Collaborative Filtering in Recommender Systems"],"prefix":"10.1145","volume":"2023","author":[{"given":"Yeon-Chang","family":"Lee","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang-Wook","family":"Kim","sequence":"additional","affiliation":[{"name":"Hanyang University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Aggarwal C. C. 2016. Recommender Systems - The Textbook.  Aggarwal C. C. 2016. Recommender Systems - The Textbook.","DOI":"10.1007\/978-3-319-29659-3"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the SIAM International Conference on Data Mining (SDM). 802--813","author":"Appel A. P.","unstructured":"Appel , A. P. , Chakrabarti , D. , Faloutsos , C. , Kumar , R. , Leskovec , J. , and Tomkins , A . 2009. Shatterplots: Fast tools for mining large graphs . In Proceedings of the SIAM International Conference on Data Mining (SDM). 802--813 . Appel, A. P., Chakrabarti, D., Faloutsos, C., Kumar, R., Leskovec, J., and Tomkins, A. 2009. Shatterplots: Fast tools for mining large graphs. In Proceedings of the SIAM International Conference on Data Mining (SDM). 802--813."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 4141--4148","author":"Bae H.","unstructured":"Bae , H. , Ahn , J. , Lee , D. , and Kim , S . 2023. LANCER: A lifetime-aware news recommender system . In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 4141--4148 . Bae, H., Ahn, J., Lee, D., and Kim, S. 2023. LANCER: A lifetime-aware news recommender system. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 4141--4148."},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of IEEE International Conference on Data Engineering (ICDE). 2822--2834","author":"Bae H.","unstructured":"Bae , H. , Lee , Y. , Han , K. , and Kim , S . 2023. A competition-aware approach to accurate TV show recommendation . In Proceedings of IEEE International Conference on Data Engineering (ICDE). 2822--2834 . Bae, H., Lee, Y., Han, K., and Kim, S. 2023. A competition-aware approach to accurate TV show recommendation. In Proceedings of IEEE International Conference on Data Engineering (ICDE). 2822--2834."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2807452"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the International Conference on Database Systems for Advanced Applications (DASFAA).","volume":"12114","author":"Chae D.","unstructured":"Chae , D. , Kang , J. , and Kim , S . 2020. Zero-injection meets deep learning: Boosting the accuracy of collaborative filtering in top-n recommendation . In Proceedings of the International Conference on Database Systems for Advanced Applications (DASFAA). Vol. 12114 . 607--620. Chae, D., Kang, J., and Kim, S. 2020. Zero-injection meets deep learning: Boosting the accuracy of collaborative filtering in top-n recommendation. In Proceedings of the International Conference on Database Systems for Advanced Applications (DASFAA). Vol. 12114. 607--620."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of The World Wide Web Conference (WWW). 2616--2622","author":"Chae D.","unstructured":"Chae , D. , Kang , J. , Kim , S. , and Choi , J . 2019. Rating augmentation with generative adversarial networks towards accurate collaborative filtering . In Proceedings of The World Wide Web Conference (WWW). 2616--2622 . Chae, D., Kang, J., Kim, S., and Choi, J. 2019. Rating augmentation with generative adversarial networks towards accurate collaborative filtering. In Proceedings of The World Wide Web Conference (WWW). 2616--2622."},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of IEEE International Conference on Data Engineering (ICDE). 316--327","author":"Cho K.","unstructured":"Cho , K. , Lee , Y. , Han , K. , Choi , J. , and Kim , S . 2019. No, that's not my feedback: TV show recommendation using watchable interval . In Proceedings of IEEE International Conference on Data Engineering (ICDE). 316--327 . Cho, K., Lee, Y., Han, K., Choi, J., and Kim, S. 2019. No, that's not my feedback: TV show recommendation using watchable interval. In Proceedings of IEEE International Conference on Data Engineering (ICDE). 316--327."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.46"},{"key":"e_1_2_1_10_1","volume-title":"Crowdstart: Warming up cold-start items using crowdsourcing. Expert Syst. Appl. 138.","author":"Hong D.","year":"2019","unstructured":"Hong , D. , Lee , Y. , Lee , J. , and Kim , S . 2019 . Crowdstart: Warming up cold-start items using crowdsourcing. Expert Syst. Appl. 138. Hong, D., Lee, Y., Lee, J., and Kim, S. 2019. Crowdstart: Warming up cold-start items using crowdsourcing. Expert Syst. Appl. 138."},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of IEEE International Conference on Data Engineering (ICDE). 349--360","author":"Hwang W.-S.","unstructured":"Hwang , W.-S. , Parc , J. , Kim , S.-W. , Lee , J. , and Lee , D . 2016. \"Told You I Didn't Like It\": Exploiting uninteresting items for effective collaborative filtering . In Proceedings of IEEE International Conference on Data Engineering (ICDE). 349--360 . Hwang, W.-S., Parc, J., Kim, S.-W., Lee, J., and Lee, D. 2016. \"Told You I Didn't Like It\": Exploiting uninteresting items for effective collaborative filtering. In Proceedings of IEEE International Conference on Data Engineering (ICDE). 349--360."},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). 629--638","author":"Jang M.-H.","unstructured":"Jang , M.-H. , Faloutsos , C. , Kim , S.-W. , Kang , U. , and Ha , J . 2016. PIN-TRUST: Fast trust propagation exploiting positive, implicit, and negative information . In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). 629--638 . Jang, M.-H., Faloutsos, C., Kim, S.-W., Kang, U., and Ha, J. 2016. PIN-TRUST: Fast trust propagation exploiting positive, implicit, and negative information. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). 629--638."},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 1044--1056","author":"Jin Y.","unstructured":"Jin , Y. , Lee , Y. , Sharma , K. , Ye , M. , Sikka , K. , Divakaran , A. , and Kumar , S . 2023. Predicting information pathways across online communities . In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 1044--1056 . 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In Proceedings of the IEEE International Conference on Data Mining (ICDM). 973--978."},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the International Conference on Extending Database Technology (EDBT). 193--204","author":"Jung J.","unstructured":"Jung , J. , Park , H. , and Kang , U . 2020. BalanSiNG: Fast and scalable generation of realistic signed networks . In Proceedings of the International Conference on Extending Database Technology (EDBT). 193--204 . Jung, J., Park, H., and Kang, U. 2020. BalanSiNG: Fast and scalable generation of realistic signed networks. In Proceedings of the International Conference on Extending Database Technology (EDBT). 193--204."},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the IEEE International Conference on Data Mining (ICDM). 1150--1155","author":"Kang Y.","unstructured":"Kang , Y. , Lee , W. , Lee , Y. , Han , K. , and Kim , S . 2021. Adversarial learning of balanced triangles for accurate community detection on signed networks . In Proceedings of the IEEE International Conference on Data Mining (ICDM). 1150--1155 . Kang, Y., Lee, W., Lee, Y., Han, K., and Kim, S. 2021. Adversarial learning of balanced triangles for accurate community detection on signed networks. In Proceedings of the IEEE International Conference on Data Mining (ICDM). 1150--1155."},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). 4138--4142","author":"Kim T.","unstructured":"Kim , T. , Kim , Y. , Lee , Y. , Shin , W. , and Kim , S . 2022. Is it enough just looking at the title?: Leveraging body text to enrich title words towards accurate news recommendation . In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). 4138--4142 . Kim, T., Kim, Y., Lee, Y., Shin, W., and Kim, S. 2022. Is it enough just looking at the title?: Leveraging body text to enrich title words towards accurate news recommendation. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). 4138--4142."},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). 993--1002","author":"Kim T.","unstructured":"Kim , T. , Lee , Y. , Shin , K. , and Kim , S . 2022. MARIO: modality-aware attention and modality-preserving decoders for multimedia recommendation . In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). 993--1002 . Kim, T., Lee, Y., Shin, K., and Kim, S. 2022. MARIO: modality-aware attention and modality-preserving decoders for multimedia recommendation. 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