A new collaborative filtering metric that improves the behavior of recommender systems

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A systematic review and research perspective on recommender systems (2022, background): “Nowadays, recommender systems are being increasingly used for a large number of applications such as web [1, 67, 70], books [2], e-learning [4, 16, 61], tourism [5, 8, 78], movies [66], music [79], e-commerce, news, specialized research resources [65], television programs [72, 81], etc.”
Recommender systems survey (2013, methodology): “A new metric (JMSD) has recently been published, which besides using the numerical information from the ratings (via mean squared differences) also uses the non-numerical information provided by the arrangement of these (via Jaccard) [31].”
A literature review and classification of recommender systems research (2012, unknown): “Heuristic model Boutemedjet and Ziou (2008) k-NN Lee, Park, and Park (2008) k-NN, link analysis Kim, Kim, and Cho (2008) k-NN Lee, Park, and Park (2009) Heuristic model, k-NN Nan Zheng, Li, Liao, and Zhang (2010)”
A collaborative filtering approach to mitigate the new user cold start problem (2012, methodology): “1 displays the arithmetic average and standard deviation distributions of the votes cast by users of Movielens 1 M and Netflix [5].”