{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:27:35Z","timestamp":1777696055798,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2021,7,9]]},"abstract":"<jats:p>With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria \u2013 such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.<\/jats:p>","DOI":"10.3233\/ida-205388","type":"journal-article","created":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T14:26:19Z","timestamp":1626186379000},"page":"1013-1029","source":"Crossref","is-referenced-by-count":41,"title":["Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation"],"prefix":"10.1177","volume":"25","author":[{"given":"Zeeshan","family":"Zeeshan","sequence":"first","affiliation":[{"name":"Kymeta Corporation, Redmond, WA, USA"}]},{"given":"Qurat","family":"ul Ain","sequence":"additional","affiliation":[{"name":"Amazon Head Office, Seattle, WA, USA"}]},{"given":"Uzair Aslam","family":"Bhatti","sequence":"additional","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing, Jiangsu, China"}]},{"given":"Waqar Hussain","family":"Memon","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China"}]},{"given":"Sajid","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Information Sciences, University of Education, Lahore, Pakistan"}]},{"given":"Saqib Ali","family":"Nawaz","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China"}]},{"given":"Mir Muhammad","family":"Nizamani","sequence":"additional","affiliation":[{"name":"Hainan University, Haikou, Hainan, China"}]},{"given":"Anum","family":"Mehmood","sequence":"additional","affiliation":[{"name":"Hainan University, Haikou, Hainan, China"}]},{"given":"Mughair Aslam","family":"Bhatti","sequence":"additional","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing, Jiangsu, China"}]},{"given":"Muhammad Usman","family":"Shoukat","sequence":"additional","affiliation":[{"name":"School of Automation and Information, Sichuan University of Science and Engineering, Yibin, Sichuan, China"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-205388_ref1","first-page":"681","article-title":"A kernel method for multi-labelled classification","author":"Elisseeff","year":"2002","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.3233\/IDA-205388_ref3","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.fss.2008.03.017","article-title":"Representation, similarity measures and aggregation methods using fuzzy sets for contentbased recommender systems","volume":"160","author":"Zenebe","year":"2009","journal-title":"Fuzzy Sets and Systems"},{"issue":"2","key":"10.3233\/IDA-205388_ref4","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s40135-015-0073-9","article-title":"What causes eye pain","volume":"3","author":"Belmonte","year":"2015","journal-title":"Current Ophthalmology Reports"},{"key":"10.3233\/IDA-205388_ref5","doi-asserted-by":"crossref","unstructured":"D. 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