University of Illinois Urbana-Champaign

Recomendr-entity recommendation based on ad-hoc dimensions

Rawlani, Preeyaa

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https://hdl.handle.net/2142/24103

Description

Title
Recomendr-entity recommendation based on ad-hoc dimensions
Author(s)
Rawlani, Preeyaa
Issue Date
2011-05-25T15:03:31Z
Director of Research (if dissertation) or Advisor (if thesis)
Zhai, ChengXiang
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(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.
Graduation Semester
2011-05
Permalink
http://hdl.handle.net/2142/24103
Copyright and License Information
Copyright 2011 Preeyaa Rawlani

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