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In the contemporary era, social media has its influence on people in makingdecisions. The proliferation of online reviews with diversified and verbosecontent often causes problems inaccurate decision making. Since online reviewshave an impact on people of all walks of life while taking decisions, choosingappropriate reviews based on the podsolization consisting is very importantsince it relies on using such micro-reviews consistency to evaluate the reviewset section. Micro-reviews are very concise and directly talk about product orservice instead of having unnecessary verbose content. Thus, micro-reviews canhelp in choosing reviews based on their personalized consistency that isrelated to directly or indirectly to the main profile of the reviews.Personalized reviews selection that is highly relevant with high personalizedcoverage in terms of matching with micro-reviews is the main problem that isconsidered in this paper. Furthermore, personalization with user preferenceswhile making review selection is also considered based on the personalizedusers' profile. Towards this end, we proposed a framework known as PeRView forpersonalized review selection using micro-reviews based on the proposedevaluation metric approach which considering two main factors (personalizedmatching score and subset size). Personalized Review Selection Algorithm (PRSA)is proposed which makes use of multiple similarity measures merged to havehighly efficient personalized reviews matching function for selection. Theexperimental results based on using reviews dataset which is collected fromYELP.COM while micro-reviews dataset is obtained from Foursqure.COM. show thatthe personalized reviews selection is a very empirical case of study.

PeRView: A Framework for Personalized Review Selection Using Micro-Reviews