{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:43:00Z","timestamp":1754156580993,"version":"3.41.2"},"reference-count":43,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T00:00:00Z","timestamp":1719187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2024,7,17]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users\u2019 latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-01-2024-0016","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T10:53:36Z","timestamp":1718880816000},"page":"577-604","source":"Crossref","is-referenced-by-count":2,"title":["UICF: a new user-item composite filtering recommendation framework by leveraging temporal semantics"],"prefix":"10.1108","volume":"17","author":[{"given":"Qingting","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3201-5412","authenticated-orcid":false,"given":"Xing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daming","family":"Xian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1619-1967","authenticated-orcid":false,"given":"Jianfeng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyang","family":"Long","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2024,6,24]]},"reference":[{"key":"key2024071612512317800_ref001","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1109\/ikt.2013.6620074","article-title":"A collaborative filtering recommender system based on user's time pattern activity","year":"2013"},{"issue":"3","key":"key2024071612512317800_ref002","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1109\/tkde.2013.7","article-title":"Typicality-based collaborative filtering recommendation","volume":"26","year":"2014","journal-title":"IEEE Transactions on Knowledge & Data Engineering"},{"key":"key2024071612512317800_ref003","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.chaos.2018.06.011","article-title":"Dynamic evolutionary clustering approach based on time weight and latent attributes for collaborative filtering recommendation","volume":"114","year":"2018","journal-title":"Chaos, Solitons Fractals"},{"issue":"04","key":"key2024071612512317800_ref004","first-page":"467","article-title":"A generative adversarial network recommendation algorithm for sparse data scenarios","volume":"51","year":"2023","journal-title":"Journal of Fuzhou University (Natural Science Edition)"},{"issue":"375","key":"key2024071612512317800_ref005","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/13638-000","article-title":"Psychology: an elementary text-book","volume":"107","year":"1908","journal-title":"Psychology: An Elementary Text-Book"},{"key":"key2024071612512317800_ref006","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1145\/3511808.3557220","article-title":"KuaiRec: a fully-observed dataset and insights for evaluating recommender systems","year":"2022"},{"key":"key2024071612512317800_ref007","first-page":"8:1","article-title":"Contextual collaborative filtering recommendation model integrated with drift characteristics of user interest","volume":"11","year":"2021","journal-title":"Human-centric Computing and Information Sciences"},{"issue":"4","key":"key2024071612512317800_ref008","doi-asserted-by":"publisher","first-page":"19:1","DOI":"10.1145\/2827872","article-title":"The MovieLens datasets: history and context","volume":"5","year":"2016","journal-title":"ACM Transactions on Interactive Intelligent Systems"},{"key":"key2024071612512317800_ref009","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/icbdci.2019.8686102","article-title":"Collaborative filtering recommendation algorithm considering users' preferences for item attributes","year":"2019"},{"issue":"4","key":"key2024071612512317800_ref010","doi-asserted-by":"publisher","first-page":"e12893:1","DOI":"10.1111\/exsy.12893","article-title":"Optimization of fuzzy similarity by genetic algorithm in user-based collaborative filtering recommender systems","volume":"39","year":"2022","journal-title":"Expert Systems: The International Journal of Knowledge Engineering"},{"issue":"8","key":"key2024071612512317800_ref011","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/j.neucom.2019.03.098","article-title":"Movie collaborative filtering with multiplex implicit feedbacks","volume":"398","year":"2019","journal-title":"Neurocomputing"},{"key":"key2024071612512317800_ref012","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1145\/3209978.3210017","article-title":"Improving sequential recommendation with knowledge-enhanced memory networks","year":"2018"},{"key":"key2024071612512317800_ref013","doi-asserted-by":"publisher","first-page":"202122","DOI":"10.1109\/access.2020.3035703","article-title":"Improved collaborative filtering recommendation through similarity prediction","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"key2024071612512317800_ref014","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.patrec.2018.12.007","article-title":"A note on the triangle inequality for the Jaccard distance","volume":"120","year":"2019","journal-title":"Pattern Recognition Letters"},{"key":"key2024071612512317800_ref015","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1109\/cyberc55534.2022.00058","article-title":"A personalized recommendation fusing tag feature and temporal context","year":"2022"},{"key":"key2024071612512317800_ref016","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.jocs.2018.03.009","article-title":"Movie recommendation based on bridging movie feature and user interest","volume":"26","year":"2018","journal-title":"Journal of Computational Science"},{"issue":"4","key":"key2024071612512317800_ref017","doi-asserted-by":"publisher","first-page":"130:1","DOI":"10.3390\/info10040130","article-title":"Combined recommendation algorithm based on improved similarity and forgetting curve","volume":"10","year":"2019","journal-title":"Information (Switzerland)"},{"key":"key2024071612512317800_ref018","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/icac3n53548.2021.9725646","article-title":"Movie recommendation system by feed forward deep neural network","year":"2021"},{"key":"key2024071612512317800_ref019","doi-asserted-by":"crossref","unstructured":"Pazzani, M.J. and Billsus, D. (2007), \u201cContent-based recommendation systems\u201d, in The Adaptive Web: Methods and Strategies of Web Personalization, Springer-Verlag, pp.\u00a0325-341.","DOI":"10.1007\/978-3-540-72079-9_10"},{"key":"key2024071612512317800_ref020","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/978-3-319-05458-2_26","article-title":"Item-based collaborative filtering with attribute correlation: a case study on movie recommendation","year":"2014"},{"first-page":"364","article-title":"Clustering collaborative filtering recommendation algorithm of users based on time factor","year":"2020","key":"key2024071612512317800_ref021"},{"issue":"1","key":"key2024071612512317800_ref022","doi-asserted-by":"publisher","first-page":"304","DOI":"10.37398\/jsr.2021.650140","article-title":"Comparative assessment of extractive summarization: TextRank, TF-IDF and LDA","volume":"65","year":"2021","journal-title":"Journal of Scientific Research"},{"key":"key2024071612512317800_ref023","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1145\/371920.372071","article-title":"Item-based collaborative filtering recommendation algorithms","year":"2001"},{"key":"key2024071612512317800_ref024","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/iaeac47372.2019.8997558","article-title":"Collaborative filtering algorithm based on user interest change","year":"2019"},{"first-page":"301","article-title":"Personalized recommendation based on knowledge map and multi feature fusion","year":"2022","key":"key2024071612512317800_ref025"},{"issue":"11","key":"key2024071612512317800_ref026","doi-asserted-by":"publisher","first-page":"2721","DOI":"10.3724\/sp.j.1001.2013.04478","article-title":"Recommendations based on collaborative filtering by exploiting sequential behaviors","volume":"24","year":"2014","journal-title":"Chines Journal of Software"},{"issue":"8","key":"key2024071612512317800_ref027","doi-asserted-by":"publisher","first-page":"e0183570:1","DOI":"10.1371\/journal.pone.0183570","article-title":"Integrating triangle and Jaccard similarities for recommendation","volume":"12","year":"2017","journal-title":"PLoS ONE"},{"key":"key2024071612512317800_ref028","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1049\/ic.2016.0032","article-title":"A Bray-Curtis strategy for boosting product recommendation","year":"2016"},{"issue":"06","key":"key2024071612512317800_ref029","first-page":"73","article-title":"Collaborative filtering recommendation algorithm based on item probability distribution","volume":"32","year":"2016","journal-title":"New Technology of Library and Information Service"},{"issue":"7","key":"key2024071612512317800_ref030","first-page":"1388","article-title":"Collaborative filtering algorithm combined with relative differences in scores","volume":"43","year":"2022","journal-title":"Journal of Chinese Computer Systems"},{"issue":"5","key":"key2024071612512317800_ref031","doi-asserted-by":"publisher","first-page":"731","DOI":"10.3758\/bf03211316","article-title":"Genuine power curves in forgetting: a quantitative analysis of individual subject forgetting functions","volume":"25","year":"2007","journal-title":"Memory & Cognition"},{"key":"key2024071612512317800_ref032","doi-asserted-by":"publisher","first-page":"1950","DOI":"10.1145\/3477495.3531785","article-title":"Denoising time cycle modeling for recommendation","year":"2022"},{"key":"key2024071612512317800_ref033","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.engappai.2015.07.012","article-title":"Collaborative recommendation with user generated content","volume":"45","year":"2015","journal-title":"Engineering Applications of Artificial Intelligence: The International Journal of Intelligent Real-Time Automation"},{"key":"key2024071612512317800_ref034","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1109\/ieec.2009.61","article-title":"An enhanced collaborative filtering algorithm based on time weight","year":"2009"},{"first-page":"3217","article-title":"STAM: a spatiotemporal aggregation method for graph neural network-based recommendation","year":"2022","key":"key2024071612512317800_ref035"},{"key":"key2024071612512317800_ref036","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1145\/1557019.1557072","article-title":"Collaborative filtering with temporal dynamics","year":"2009"},{"key":"key2024071612512317800_ref037","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/icicse52190.2021.9404120","article-title":"Collaborative filtering recommendation with fluctuations of user\u2019 preference","year":"2021"},{"issue":"6","key":"key2024071612512317800_ref038","first-page":"1898","article-title":"Review of recommendation system","volume":"42","year":"2022","journal-title":"Journal of Computer Applications"},{"key":"key2024071612512317800_ref039","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1109\/iitsi.2010.161","article-title":"A collaborative filtering algorithm based on time period partition","year":"2010"},{"issue":"1","key":"key2024071612512317800_ref040","doi-asserted-by":"publisher","first-page":"020167:1","DOI":"10.1063\/1.4982532","article-title":"Improved collaborative filtering recommendation algorithm of similarity measure","volume":"1839","year":"2017","journal-title":"AIP Conference Proceedings"},{"key":"key2024071612512317800_ref041","doi-asserted-by":"publisher","first-page":"9454","DOI":"10.1109\/access.2018.2789866","article-title":"A recommendation model based on deep neural network","volume":"6","year":"2018","journal-title":"IEEE Access"},{"issue":"5","key":"key2024071612512317800_ref042","doi-asserted-by":"publisher","first-page":"4741","DOI":"10.1109\/tkde.2022.3151618","article-title":"Dynamic graph neural networks for sequential recommendation","volume":"35","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"key2024071612512317800_ref043","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1109\/itoec49072.2020.9141788","article-title":"Collaborative filtering recommendation algorithm based on improved similarity","year":"2020"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-01-2024-0016\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-01-2024-0016\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:54:02Z","timestamp":1753397642000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/17\/3\/577-604\/1228078"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,24]]},"references-count":43,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,6,24]]},"published-print":{"date-parts":[[2024,7,17]]}},"alternative-id":["10.1108\/IJICC-01-2024-0016"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-01-2024-0016","relation":{},"ISSN":["1756-378X"],"issn-type":[{"type":"print","value":"1756-378X"}],"subject":[],"published":{"date-parts":[[2024,6,24]]}}}