Yissum - Research Development Company of the Hebrew University

Automatic Algorithm for Ranking Most Helpful Product Reviews

Posted by Yissum - Research Development Company of the Hebrew UniversityResponsive · Innovative Products and Technologies · Israel

Summary of the technology

Cluster3
Meeting with the researcher was held on 20/5/2008. Forums, reviews, ranking
Multi-Layer Lexical Model for Automatically Ranking Book Reviews According to Review Helpfulness
Project ID : 10-2008-2072

Yissum - Research Development Company of the Hebrew University

Description of the technology

A multi-layered approach for ranking book and product reviews

Categories

Data Mining

Development Stage

Algorithm development complete, Working Prototype on Amazon Reviews

Market

Internet consumer market

Highlights

  • A system for sorting through thousands of book reviews to rank the most helpful
  • Identifies helpful information in reviews and automatically ranks them
  • Human evaluators’ choice of most helpful reviews corresponded with the MLLM’s choice 85% of the time
  • Can be used for all types of product reviews, such as consumer electronics
  • Overcomes biases found in user voting mechanisms

Our Innovation

  • A Multi Layer Lexical Model (MLLM)-based algorithm for ranking book reviews. The MMLM approach is a system for data mining and content analysis that examines book reviews in order to establish which of the reviews are the most helpful. If available, the text of the book itself can also be used to enhance the output. The layers contain compact, high-quality lexicons of words specific for each layer, such as terms common in product reviews, specific lexical terms connected with the type of book and terms connected with the title.

Key Features

  • System outperforms voter ranking and random sampling
  • System provides a continuous scale of grading
  • Allows helpful reviews that may potentially be overlooked to be identified
  • Can easily adapt the review ranking to match different criteria, such as review length
  • Fully unsupervised approach precludes the need for human annotations. does not depend on active users– reduces costs

Development Milestones

  • System was developed using books that had large numbers of reviews. Future development will be for a system that works where there are fewer reviews
  • The MLLM approach will be used to generate a single comprehensive review from the reviews ranked most helpful

The Opportunity

  • Can be applied to reviews of all sorts of products to assist consumers make purchasing decisions
Link to articlewww.yissum.co.il/sites/default/files/project_images/Articles/2072_-_revrank.pdf

Project manager

Aviv Shoher
SVP BUSINESS DEVELOPMENT

Project researchers

Ari Rappoport
HUJI, School of Computer Science and Engineering
Computer Science

Oren Tsur
HUJI, School of Computer Science and Engineering

Related keywords

  • Information Processing, Information System, Workflow Management
  • IT and Telematics Applications
  • Multimedia
  • Computers
  • Computer Graphics Related
  • Specialised Turnkey Systems
  • Scanning Related
  • Peripherals
  • Computer Services
  • Computer Software Market
  • Other Computer Related
  • Computer Science & Engineering
  • Web Technologies

About Yissum - Research Development Company of the Hebrew University

Technology Transfer Office from Israel

Yissum Research Development Company of the Hebrew University of Jerusalem Ltd. Founded in 1964 to protect and commercialize the Hebrew University’s intellectual property. Ranked among the top technology transfer companies, Yissum has registered over 8,900 patents covering 2,500 inventions; has licensed out 800 technologies and has spun-off 90 companies. Products that are based on Hebrew University technologies and were commercialized by Yissum generate today over $2 Billion in annual sales.

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