{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T06:14:40Z","timestamp":1769062480538,"version":"3.49.0"},"reference-count":56,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2020,6,21]],"date-time":"2020-06-21T00:00:00Z","timestamp":1592697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Industry Projects in Jiangsu S8T Pillar Program","award":["BE2019110"],"award-info":[{"award-number":["BE2019110"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["71571093, 91646204, and 71801123"],"award-info":[{"award-number":["71571093, 91646204, and 71801123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Program of China","award":["2016YFB1000901"],"award-info":[{"award-number":["2016YFB1000901"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>The association-rule-based approach is one of the most common technologies for building recommender systems and it has been extensively adopted for commercial use. A variety of techniques, mainly including eligible rule selection and multiple rules combination, have been developed to create effective recommendation. Unfortunately, little attention has been paid to the scalability concern of rule-based recommendation methods. However, the computational complexity of rule-based methods shall increase drastically with the growth of both online customers and rules, which are usually several millions in typical e-commerce platforms. Moreover, the dynamic change of users\u2019 actions requires rule-based methods make recommendations in nearly real-time, which further highlights the scalability issue of rule-based recommender systems. In this article, we present a distributed framework that can scale different association-rule-based recommendation methods in a unified way. Specifically, based on the summarization of existing rule-based approaches, a generic tree-type structure is defined to store separate kinds of patterns, and an efficient algorithm is designed for mining eligible patterns along with computing recommendation scores. To handle the ever-increasing number of online customers, a distributed framework is proposed, where two load-balanced strategies for partitioning tree are put forward to fit sparse and dense data, respectively. Extensive experiments on five real-life data sets demonstrate that the efficiency of association-rule-based recommender systems can be significantly improved by the proposed framework.<\/jats:p>","DOI":"10.1145\/3398202","type":"journal-article","created":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T02:45:41Z","timestamp":1592793941000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":44,"title":["On Scalability of Association-rule-based Recommendation"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0591-1861","authenticated-orcid":false,"given":"Zhiang","family":"Wu","sequence":"first","affiliation":[{"name":"Nanjing Audit University, Nanjing, China"}]},{"given":"Changsheng","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University of Finance and Economics, Nanjing, China"}]},{"given":"Jie","family":"Cao","sequence":"additional","affiliation":[{"name":"Nanjing University of Finance and Economics, Nanjing, China"}]},{"given":"Yong","family":"Ge","sequence":"additional","affiliation":[{"name":"University of Arizona, Tucson, Arizona, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,6,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009839827683"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.1050.0153"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","volume-title":"Recommender Systems","author":"Aggarwal Charu C.","DOI":"10.1007\/978-3-319-29659-3"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150416"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835449.1835467"},{"key":"e_1_2_1_8_1","volume-title":"Introduction to Algorithms","author":"Cormen Thomas H."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864770"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079628.3079699"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.06.010"},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM\u201901)","volume":"3","author":"Campagnani Gama Gustavo Machado"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2015.0583"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.2013.0575"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1137\/0117039"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2004.10116"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.166"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2856037"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335372"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2365952.2365979"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the IEEE \/ WIC International Conference on Web Intelligence (WI\u201903)","author":"Kim Choonho","year":"2003"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3106426.3106542"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-006-0002-1"},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the ACM Conference on Recommender Systems (RecSys\u201908)","author":"Li Haoyuan"},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of the IEEE International Conference on Data Mining. 19--21","author":"Li Wenmin","year":"2001"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013284820704"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2003.1167344"},{"key":"e_1_2_1_28_1","first-page":"3505","article-title":"A hybrid of sequential rules and collaborative filtering for product recommendation. 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