{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T18:27:11Z","timestamp":1770488831897,"version":"3.49.0"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T00:00:00Z","timestamp":1588896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With the advent of e-commerce sites and social media, users express their preferences and tastes freely through user-generated content such as reviews and comments. In order to promote cross-selling, e-commerce sites such as eBay and Amazon regularly use such inputs from multiple domains and suggest items with which users may be interested. In this paper, we propose a topic coherence-based cross-domain recommender model. The core concept is to use topic modeling to extract topics from user-generated content such as reviews and combine them with reliable semantic coherence techniques to link different domains, using Wikipedia as a reference corpus. We experiment with different topic coherence methods such as pointwise mutual information (PMI) and explicit semantic analysis (ESA). Experimental results presented demonstrate that our approach, using PMI as topic coherence, yields 22.6% and using ESA yields 54.4% higher precision as compared with cross-domain recommender system based on semantic clustering.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaa008","type":"journal-article","created":{"date-parts":[[2020,1,18]],"date-time":"2020-01-18T12:08:43Z","timestamp":1579349323000},"page":"80-90","source":"Crossref","is-referenced-by-count":9,"title":["Enriching Topic Coherence on Reviews for Cross-Domain Recommendation"],"prefix":"10.1093","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6620-5098","authenticated-orcid":false,"given":"Mala","family":"Saraswat","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Netaji Subhas University of Technology (NSUT), Dwarka Sector-3, Dwarka, Delhi, 110078, India"},{"name":"Department of Computer Science and Engineering, ABES Engineering College, Campus -1, 19th KM Stone, NH 24, Ghaziabad, Uttar Pradesh 201009"}]},{"given":"Shampa","family":"Chakraverty","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Netaji Subhas University of Technology (NSUT), Dwarka Sector-3, Dwarka, Delhi, 110078, India"}]}],"member":"286","published-online":{"date-parts":[[2020,5,8]]},"reference":[{"key":"2022011721234212800_ref1","first-page":"100","volume-title":"Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics","author":"Newman","year":"2010"},{"key":"2022011721234212800_ref2","first-page":"11","article-title":"External evaluation of topic models","volume":"2009","author":"Newman","year":"2009","journal-title":"Australasian Doc. 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