[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127029-en":3,"doc-seo-127029-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127029,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Systematic Review of Machine Learning in Recommendation Systems Over the Last Decade","This study provides a comprehensive overview of machine-learning approaches used in recommendation systems over the last decade. The review draws mainly on two filtering categories: content-based filtering and collaborative filtering, and examines about forty published articles. Results show collaborative filtering is used more frequently (70% vs 23%), with remaining studies applying hybrid combinations. Across machine-learning methods, supervised learning dominates, while K-means is the most commonly adopted algorithm. Cosine similarity emerges as the leading evaluation metric.","Wrexham University Research Online  \nConference Paper  \nSystematic Review of Machine Learning in Recommendation Systems Over the Last Decade  \nWeiner, F., Teh, P.L., Cheng, CB.  \nThis is a paper presented at the Intelligent Computing, SAI 2024.  \nCopyright of the author(s) . Reproduced here with their permission and the permission of the conference organisers.  \nRecommended citation:  \nWeiner, F., Teh, P. L., Cheng, CB. (2024), 'Systematic Review of Machine Learning in Recommendation Systems Over the Last Decade', In Proc: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networks and Systems, vol 1016. Springer, Cham. doi: 10. 1007/978-3-031-62281-6_5.  \nThe final version of this paper is available here: [https://link.springer.com/chapter/10.1007/978-3-](https://link.springer.com/chapter/10.1007/978-3-)  \n031-62281-6_5  \nTo cite this paper:  \nWeiner, F., Teh, P.L., Cheng, CB. (2024) . Systematic Review of Machine Learning in Recommendation Systems Over the Last Decade. In: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networksand Systems, vol 1016. Springer, Cham. [https://doi.org/10.1007/978-3-031-](https://doi.org/10.1007/978-3-031-)[ ](https://doi.org/10.1007/978-3-031-)[62281-6_5](62281-6_5)  \nSystematic Review of Machine Learning in Recommendation Systems over the Last  \nDecade  \nFelix Weiner 1; Phoey Lee Teh2 and Chi-Bin Cheng3  \n1,2 Faculty of Art, Computing and Engineering, Wrexham University, UK.  \n2 Department of Information Management, Tamkang University, New Taipei City, 251301, Taiwan.  \n1,[2](2 phoey.teh@glyndwr.ac.uk)[ phoey.teh@glyndwr.ac.uk](2 phoey.teh@glyndwr.ac.uk); [3](3 cbcheng@mail.tku.edu.tw)[ cbcheng@mail.tku.edu.tw](3 cbcheng@mail.tku.edu.tw)  \n[Abstract](Abstract). This study presents a comprehensive overview of the approaches employed in recommendation systems over the last decade. The review primarily draws from two categories of filtering techniques: content-based filtering and collaborative filtering methods. We have reviewed and tabulated approximately forty articles that have been published. Major findings include: 1) collaborative filtering is more often used than content-based filtering, 70% to 23%, the rest is hybrid methods of these two; 2) more than half of the machine learning approaches adopted are supervised learning; however, 3) algorithm-wise, K-means the unsupervised learning algorithm emerged as the most frequently adopted approach in recommendation systems. Also notably, cosine similarity stands out asthe prevalent measurement technique.   \nKeywords: Content-based filtering, Collaborative filtering, Recommendation  \nSystems, K-Mean Clustering techniques.  \n1 Introduction  \nRecommendation systems aim to suggest or recommend additional products or content to consumers based on their past purchasing behaviors and preferences. Machine learning is the frequently used approach for constructing recommendation systems , as their capability of analyzing large sets of data. With the increasing popularities of online shopping and online streaming contents, there has been growing interest among computer scientists and data scientists to develop the best approaches and processes for recommendation systems.  \nTo achieve optimal performance, recommendation systems must be able to effectively handle large and complex datasets, as well as incorporate a range of different data sources and algorithms. In recent years, machine learning has played an increasingly significant role in developing more sophisticated and accurate recommendation systems, including approaches like collaborative filtering, content-based filtering, and hybrid systems that combine multiple approaches.  \n2 Literature review  \nTable 1 summarizes the approaches used in the research and project works related to recommendation systems between the years 2012 to 2023.  \nTable 1. Summary of approaches used in the past decade  \n\n| Year | Categories | Approach & Evaluation Method | Type |\n| --- | --- | -","cbCaieFuPcGpWEML","https://ap.wps.com/l/cbCaieFuPcGpWEML","pdf",509664,1,12,"English","en",105,"# Abstract\n# Introduction\n# Literature review","[{\"question\":\"What two main filtering categories does the review focus on?\",\"answer\":\"The review primarily focuses on content-based filtering and collaborative filtering methods for recommendation systems.\"},{\"question\":\"How does the review compare the usage frequency of collaborative vs. content-based filtering?\",\"answer\":\"Collaborative filtering is reported as more commonly used than content-based filtering, at roughly 70% versus 23%, with the remainder being hybrid approaches.\"},{\"question\":\"Which machine-learning approach and measurement technique are most frequently highlighted?\",\"answer\":\"Supervised learning is the most common machine-learning adoption overall, K-means is the most frequently used algorithm, and cosine similarity is the most prevalent measurement technique.\"}]","Systematic Review of Machine Learning in Recommendation Systems Over the Last Decade | 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