Customized ranking by user preference using LRR model

In this thesis, we proposed a customized ranking system that can rank all the entities given a specific user preference. Rank entities by user’s preference is an inevitable strategy of saving user’s time browsing and extracting useful information from Internet. Modern websites always rank these entities by a single numeric value computed by averaging overall rating, but this ranking scheme is of limited use to users. With di↵erent aspect preference, it is obvious that the restaurants ranking should be di↵erent based on their famous features, e.g., service, environment, price. We used the LRR (Latent Rating Regression) model to aggregate restaurants aspect score and proposed two ranking approaches. The experiment results show that the two ranking approaches are both better than the baseline ranking approach.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Chiang, Bo-Yu

• Latent Aspect Rating Analysis (LARA)
• Recommendation system

Statement dc:rights

• Copyright 2015 Bo Yu Chiang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/78480

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/78480