Rating prediction based on combination of review mining and user preference analysis

2021 ◽  
Vol 99 ◽  
pp. 101742
Author(s):  
Chin-Hui Lai ◽  
Chia-Yu Hsu
Keyword(s):  
User Preference ◽  
Preference Analysis ◽  
Review Mining ◽  
Rating Prediction

User Preference Analysis for Most Frequent Peer/Dominator

2019 ◽  
Vol 31 (7) ◽  
pp. 1412-1425
Author(s):  
Ke Deng ◽  
Yanhua Li ◽  
Jia Zeng ◽  
Mingxuan Yuan ◽  
Jun Luo ◽  
...  
Keyword(s):  
User Preference ◽  
Preference Analysis

Recommending personalized events based on user preference analysis in event based social networks

2019 ◽  
Author(s):  
Kyoungsoo Bok ◽  
Suji Lee ◽  
Dojin Choi ◽  
Donggeun Lee ◽  
Jaesoo Yoo
Keyword(s):  
Social Networks ◽  
User Preference ◽  
Preference Analysis ◽  
Event Based

Hotel recommendation based on user preference analysis

2015 ◽  
Author(s):  
Kai Zhang ◽  
Keqiang Wang ◽  
Xiaoling Wang ◽  
Cheqing Jin ◽  
Aoying Zhou
Keyword(s):  
User Preference ◽  
Preference Analysis

scholarly journals Rating Prediction Based on Merge-CNN and Concise Attention Review Mining

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 190934-190945
Author(s):  
Yun-Cheng Chou ◽  
Hsing-Yu Chen ◽  
Duen-Ren Liu ◽  
Der-Shiuan Chang
Keyword(s):  
Review Mining ◽  
Rating Prediction

Toward Comprehensive User and Item Representations via Three-tier Attention Network

2021 ◽  
Vol 39 (3) ◽  
pp. 1-22
Author(s):  
Hongtao Liu ◽  
Wenjun Wang ◽  
Qiyao Peng ◽  
Nannan Wu ◽  
Fangzhao Wu ◽  
...  
Keyword(s):  
User Preference ◽  
Product Reviews ◽  
Context Aware ◽  
Multiple Features ◽  
Attention Network ◽  
Word Level ◽  
Item Characteristics ◽  
Rich Information ◽  
Rating Prediction

Product reviews can provide rich information about the opinions users have of products. However, it is nontrivial to effectively infer user preference and item characteristics from reviews due to the complicated semantic understanding. Existing methods usually learn features for users and items from reviews in single static fashions and cannot fully capture user preference and item features. In this article, we propose a neural review-based recommendation approach that aims to learn comprehensive representations of users/items under a three-tier attention framework. We design a review encoder to learn review features from words via a word-level attention, an aspect encoder to learn aspect features via a review-level attention, and a user/item encoder to learn the final representations of users/items via an aspect-level attention. In word- and review-level attentions, we adopt the context-aware mechanism to indicate importance of words and reviews dynamically instead of static attention weights. In addition, the attentions in the word and review levels are of multiple paradigms to learn multiple features effectively, which could indicate the diversity of user/item features. Furthermore, we propose a personalized aspect-level attention module in user/item encoder to learn the final comprehensive features. Extensive experiments are conducted and the results in rating prediction validate the effectiveness of our method.

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