{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:35:37Z","timestamp":1754156137312,"version":"3.41.2"},"reference-count":36,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2016,6,20]],"date-time":"2016-06-20T00:00:00Z","timestamp":1466380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2016,6,20]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to improve the recommendations performance for cold-start users and controversial items. Collaborative filtering (CF) generates recommendations on the basis of similarity between users. It uses the opinions of similar users to generate the recommendation for an active user. As a similarity model or a neighbor selection function is the key element for effectiveness of CF, many variations of CF are proposed. However, these methods are not very effective, especially for users who provide few ratings (i.e. cold-start users).<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>A new user similarity model is proposed that focuses on improving recommendations performance for cold-start users and controversial items. To show the validity of the authors\u2019 similarity model, they conducted some experiments and showed the effectiveness of this model in calculating similarity values between users even when only few ratings are available. In addition, the authors applied their user similarity model to a recommender system and analyzed its results.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Experiments on two real-world data sets are implemented and compared with some other CF techniques. The results show that the authors\u2019 approach outperforms previous CF techniques in coverage metric while preserves accuracy for cold-start users and controversial items.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>In the proposed approach, the conditions in which CF is unable to generate accurate recommendations are addressed. These conditions affect CF performance adversely, especially in the cold-start users\u2019 condition. The authors show that their similarity model overcomes CF weaknesses effectively and improve its performance even in the cold users\u2019 condition.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-07-2015-0024","type":"journal-article","created":{"date-parts":[[2016,6,30]],"date-time":"2016-06-30T10:21:33Z","timestamp":1467282093000},"page":"126-149","source":"Crossref","is-referenced-by-count":8,"title":["Improving recommender systems\u2019 performance on cold-start users and controversial items by a new similarity model"],"prefix":"10.1108","volume":"12","author":[{"given":"Masoud","family":"Mansoury","sequence":"first","affiliation":[]},{"given":"Mehdi","family":"Shajari","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"year":"2000","key":"key2020121503102645300_ref001","article-title":"Supporting trust in virtual 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