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
相关论文
共 50 条
  • [1] Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
    Cheng, Zhiyong
    Ding, Ying
    Zhu, Lei
    Kankanhalli, Mohan
    WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), 2018, : 639 - 648
  • [2] Aspect-based latent factor model by integrating ratings and reviews for recommender system
    Qiu, Lin
    Gao, Sheng
    Cheng, Wenlong
    Guo, Jun
    KNOWLEDGE-BASED SYSTEMS, 2016, 110 : 233 - 243
  • [3] Tourism Contextual Information for Recommender System
    Achmad, Kusuma Adi
    Nugroho, Lukito Edi
    Widyawan
    Djunaedi, Achmad
    2017 7TH INTERNATIONAL ANNUAL ENGINEERING SEMINAR (INAES), 2017, : 94 - 99
  • [4] Contextual Information based Recommender System using Singular Value Decomposition
    Gupta, Rahul
    Jain, Arpit
    Rana, Satakshi
    Singh, Sanjay
    2013 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2013, : 2084 - 2089
  • [5] Understanding and Using Contextual Information in Recommender Systems
    Wang, Licai
    PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), 2011, : 1329 - 1329
  • [6] Large-scale recommender system with compact latent factor model
    Liu, Chien-Liang
    Wu, Xuan-Wei
    EXPERT SYSTEMS WITH APPLICATIONS, 2016, 64 : 467 - 475
  • [7] Latent Factor Model Applied to Recommender System: Realization, Steps and Algorithm
    Jallouli, Maryam
    Lajmi, Sonia
    Amous, Ikram
    INFORMATION SYSTEMS, EMCIS 2017, 2017, 299 : 606 - 618
  • [8] Effective rating prediction based on selective contextual information
    Dridi, Rim
    Zammali, Saloua
    Alsulimani, Tagreed
    Arour, Khedija
    INFORMATION SCIENCES, 2020, 510 : 218 - 242
  • [9] Joint Aspect Discovery, Sentiment Classification, Aspect-Level Ratings and Weights Approximation for Recommender Systems by Rating Supervised Latent Topic Model
    Ou, Wei
    Van-Nam Huynh
    JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2018, 22 (01) : 17 - 26
  • [10] Incorporating Contextual Information into a Mobile Advertisement Recommender System
    Zhu, Ke
    Xiao, Yingyuan
    Ai, Pengqiang
    Wang, Hongya
    Hsu, Ching-Hsien
    WEB TECHNOLOGIES AND APPLICATIONS (APWEB 2015), 2015, 9313 : 879 - 882
12345