INFORMATION CHANGE THE WORLD

International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.13, No.3, Jun. 2021

A Survey on Hybrid Recommendation Engine for Businesses and Users

Full Text (PDF, 317KB), PP.22-29


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Author(s)

Spurthy Mutturaj, Shwetha B, Sangeetha P, Shivani Beldale, Sahana V

Index Terms

Gensim, LDA, Recommendation System, Topic Modelling

Abstract

Various techniques have been used over the years to implement recommendation systems. In this research, we have analyzed several papers and majority of them have used collaborative and content-based filtering techniques to implement recommender system. To build a recommender system, we require abundant amount of data which comprises of a spectrum of reviews and sentiments from all user domains. Websites like Yelp and TripAdvisor, allow users to post reviews for various businesses, products and services. In this work we have two objectives 1) Recommend restaurants to user based on user reviews in Yelp dataset and 2) Suggest improvements to business based on user reviews. In the first scenario, we will use the comments and ratings available in   the Yelp dataset to generate restaurant recommendations and personalize them with user profile data. In the second scenario, we intend to suggest improvements to businesses based on various user reviews and provide them with a ranked list of predefined parameters to help them understand where they stand with respect to their competitors and where they should improve to do better. For both scenarios, we will perform two major steps to achieve our objective 1) Sentiment Analysis and 2) Content Based Recommendation. The first step gives   us the - sentiment, quality, subject of discussion relevant to product and in the second step we use the outcomes of first step for personalizing and ranking our results. We came across Gensim and Latent Dirichlet Allocation which seemed to be interesting and was tailored to our requirements. In the yelp dataset, user comments are a mixture of various topics which are extracted by the algorithm (LDA) to provide accurate recommendation for all the users. A prototype of this method provided us with 93% accuracy.

Cite This Paper

Spurthy Mutturaj, Shwetha B, Sangeetha P, Shivani Beldale, Sahana V, " A Survey on Hybrid Recommendation Engine for Businesses and Users", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.13, No.3, pp. 22-29, 2021. DOI: 10.5815/ijieeb.2021.03.03

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