A Restaurant Recommendation System by Analyzing Ratings and Aspects in Reviews

被引：5

作者：

Gao, Yifan ^{[1
]}

Yu, Wenzhe ^{[1
]}

Chao, Pingfu ^{[1
]}

Zhang, Rong ^{[1
]}

Zhou, Aoying ^{[1
]}

Yang, Xiaoyan ^{[2
]}

机构：

[1] E China Normal Univ, Inst Data Sci & Engn, Shanghai Key Lab Trustworthy Comp, Shanghai 200062, Peoples R China

[2] Illinois Singapore Pte Ltd, Adv Digital Sci Ctr, Singapore, Singapore

来源：

DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2015, PT II | 2015年 / 9050卷

关键词：

Recommender systems; Review analysis; Hidden aspect; Regression model;

D O I：

10.1007/978-3-319-18123-3_33

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recommender systems are widely deployed to predict the preferences of users to items. They are popular in helping users find movies, books and products in general. In this work, we design a restaurant recommender system based on a novel model that captures correlations between hidden aspects in reviews and numeric ratings. It is motivated by the observation that a user's preference against an item is affected by different aspects discussed in reviews. Our method first explores topic modeling to discover hidden aspects from review text. Profiles are then created for users and restaurants separately based on aspects discovered in their reviews. Finally, we utilize regression models to detect the user-restaurant relationship. Experiments demonstrate the advantages.

引用

页码：526 / 530

页数：5