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Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>\n            <jats:italic>Item-based models<\/jats:italic>\n            are among the most popular collaborative filtering approaches for building recommender systems.\n            <jats:italic>Random walks<\/jats:italic>\n            can provide a powerful tool for harvesting the rich network of interactions captured within these models. They can exploit indirect relations between the items, mitigate the effects of sparsity, ensure wider itemspace coverage, as well as increase the diversity of recommendation lists. Their potential however, can be hindered by the tendency of the walks to rapidly concentrate towards the central nodes of the graph, thereby significantly restricting the range of\n            <jats:italic>K<\/jats:italic>\n            -step distributions that can be exploited for personalized recommendations. In this work, we introduce\n            <jats:italic>RecWalk<\/jats:italic>\n            ; a novel random walk-based method that leverages the spectral properties of\n            <jats:italic>nearly uncoupled Markov chains<\/jats:italic>\n            to provably lift this limitation and prolong the influence of users\u2019 past preferences on the successive steps of the walk\u2014thereby allowing the walker to explore the underlying network more fruitfully. A comprehensive set of experiments on real-world datasets verify the theoretically predicted properties of the proposed approach and indicate that they are directly linked to significant improvements in top-\n            <jats:italic>n<\/jats:italic>\n            recommendation accuracy. They also highlight RecWalk\u2019s potential in providing a framework for boosting the performance of item-based models. RecWalk achieves state-of-the-art top-\n            <jats:italic>n<\/jats:italic>\n            recommendation quality outperforming several competing approaches, including recently proposed methods that rely on deep neural networks.\n          <\/jats:p>","DOI":"10.1145\/3406241","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T04:10:30Z","timestamp":1601352630000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks"],"prefix":"10.1145","volume":"14","author":[{"given":"Athanasios N.","family":"Nikolakopoulos","sequence":"first","affiliation":[{"name":"University of Minnesota, Minneapolis, Minnesota"}]},{"given":"George","family":"Karypis","sequence":"additional","affiliation":[{"name":"University of Minnesota, Minneapolis, Minnesota"}]}],"member":"320","published-online":{"date-parts":[[2020,9,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"volume-title":"Essays on the Structure of Social Science Models","author":"Ando Albert","key":"e_1_2_1_2_1","unstructured":"Albert Ando , Franklin M. 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