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Review-Based Topic Distribution Profile for Recommender Systems

  • Mala SaraswatEmail author
  • Shampa Chakraverty
  • Agrim Sharma
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 612)

Abstract

Using social media and e-commerce sites, users convey their preferences and interests via reviews, feedback, and comments. These comments and reviews consist of details about a given product or an item and also users’ thoughts. Different features of user-generated content include various features such as emotions, sentiments, review usefulness, and so forth that exhibit a promising exploration in the domain of recommendation frameworks. This paper harness reviews as the content generated from user to exploit topics based on topic modeling using latent Dirichlet allocation for generating topic distribution profile of users. Examination of topic distribution profile of users gives us a new prospect for recommendation of products which is based on hidden thematic framework of user preferences. Assessment on books and movie dataset confirms the adequacy of the suggested topic distribution profile for recommendation system framework.

Keywords

Reviews User-generated content Recommender systems Collaborative filtering Topic modeling 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Mala Saraswat
    • 1
    Email author
  • Shampa Chakraverty
    • 1
  • Agrim Sharma
    • 2
  1. 1.Department of Computer EngineeringNSUTNew DelhiIndia
  2. 2.Department of Information TechnologyNSUTNew DelhiIndia

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