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Title:Recomendr-entity recommendation based on ad-hoc dimensions
Author(s):Rawlani, Preeyaa
Advisor(s):Zhai, ChengXiang
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):RECOMENDR
entity
ad hoc dimensions
dimensions
features
reviews
User generated contents (UGC)
Opinion mining
recommendation system
recommendation
web crawling
polarity analysis
sentence polarity analysis
positive and negative
words
opinion rich resources
comparison
logistic regression
reciprocal rank
Normalized discounted cumulative gain (NDCG)
Abstract:The growing availability and popularity of opinion rich resources on the online web resources, such as review sites and personal blogs, has made it convenient to find out about the opinions and experiences of layman people. But, simultaneously, this huge eruption of data has made it difficult to reach to a conclusion. In this thesis, I develop a novel recommendation system, Recomendr that can help users digest all the reviews about an entity and compare candidate entities based on ad-hoc dimensions specified by keywords. It expects keyword specified ad-hoc dimensions/features as input from the user and based on those features; it compares the selected range of entities using reviews provided on the related User Generated Contents (UGC) e.g. online reviews. It then rates the textual stream of data using a scoring function and returns the decision based on an aggregate opinion to the user. Evaluation of Recomendr using a data set in the laptop domain shows that it can effectively recommend the best laptop as per user-specified dimensions such as price. Recomendr is a general system that can potentially work for any entities on which online reviews or opinionated text is available.
Issue Date:2011-05-25
URI:http://hdl.handle.net/2142/24103
Rights Information:Copyright 2011 Preeyaa Rawlani
Date Available in IDEALS:2011-05-25
Date Deposited:2011-05


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