A fusion of aspect and contextual information for rating prediction in recommender system using a latent factor model

被引：1
|
作者：
Patel J. [1 ]
Chhinkaniwala H. [2 ]
机构：
[1] Ganpat University - Institute of Technology, North Gujarat
[2] Adani Institute of Infrastructure (AII), Gujarat, Ahmedabad
来源：
International Journal of Web Engineering and Technology | 2021年 / 16卷 / 01期
关键词：
Aspect extraction; Context-aware recommender system; Latent factor model; Recommender system; Sentiment analysis; Spam detection;
D O I：
10.1504/IJWET.2021.115687
中图分类号：
学科分类号：
摘要：
Referring to reviews, checking online comments and, visiting different websites before buying any product is a call of the day. Online reviews are an excellent source of information both for users and organisations alike. In this article, a hybrid model, named as aspect and context-based latent factor model (ACMF), is proposed to predict user rating on an item based on star ratings provided by users, feature-opinion information, and context information. ACMF mainly consists of three phases: the first phase extracts spam reviews, the second phase extracts features and opinions from written reviews and calculates the polarity score of opinions. In the last phase, reviews and context information are aggregated to predict the unknown rating of a user for better recommendations. The proposed model is tested on ratings and reviews downloaded from the Amazon website. Experiment results show RMSE of ACMF has been achieved significantly less than other relevant methods. Copyright © 2021 Inderscience Enterprises Ltd.
引用
收藏
页码：30 / 52
页数：22
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