{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/perview-a-framework-for-personalized-review","title":"PeRView: A Framework for Personalized Review Selection Using Micro-Reviews","arxiv_id":"1804.08234","date":"2018-04-23","proceeding":null,"authors":["Muhmmad Al-Khiza'ay","Noora Alallaq","Qusay Alanoz","Adil Al-Azzawi","N. Maheswari"],"abstract":"In the contemporary era, social media has its influence on people in making\ndecisions. The proliferation of online reviews with diversified and verbose\ncontent often causes problems inaccurate decision making. Since online reviews\nhave an impact on people of all walks of life while taking decisions, choosing\nappropriate reviews based on the podsolization consisting is very important\nsince it relies on using such micro-reviews consistency to evaluate the review\nset section. Micro-reviews are very concise and directly talk about product or\nservice instead of having unnecessary verbose content. Thus, micro-reviews can\nhelp in choosing reviews based on their personalized consistency that is\nrelated to directly or indirectly to the main profile of the reviews.\nPersonalized reviews selection that is highly relevant with high personalized\ncoverage in terms of matching with micro-reviews is the main problem that is\nconsidered in this paper. Furthermore, personalization with user preferences\nwhile making review selection is also considered based on the personalized\nusers' profile. Towards this end, we proposed a framework known as PeRView for\npersonalized review selection using micro-reviews based on the proposed\nevaluation metric approach which considering two main factors (personalized\nmatching score and subset size). Personalized Review Selection Algorithm (PRSA)\nis proposed which makes use of multiple similarity measures merged to have\nhighly efficient personalized reviews matching function for selection. The\nexperimental results based on using reviews dataset which is collected from\nYELP.COM while micro-reviews dataset is obtained from Foursqure.COM. show that\nthe personalized reviews selection is a very empirical case of study.","url_abs":"http://arxiv.org/abs/1804.08234v1","url_pdf":"http://arxiv.org/pdf/1804.08234v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"perview-a-framework-for-personalized-review","repo_url":"https://github.com/jja-bot/jamiegogo/blob/master/layouts/partials/contact.html","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}