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

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

Index Terms

Gensim, LDA, Recommendation System, Topic Modelling


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


[1]Lopamudra Dey, Sanjay Chakraborty, Anuraag Biswas, Beepa Bose, Sweta Tiwari,"Sentiment Analysis of Review Datasets Using Naïve Bayes' and K-NN Classifier", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.8, No.4, pp.54-62, 2016. DOI: 10.5815/ijieeb.2016.04.07.

[2]Prof Vipul vekariya and Dr G R Kulkarni. Hybrid Recommender systems: survey and Experiments. Journal of information, knowledge and research in computer engineering 2012.

[3]Tri Doan and Jugal Kalita. Sentiment Analysis of Restaurant Reviews on Yelp with Incremental Learning. 2016 15th IEEE International Conference on Machine Learning and Applications.

[4]Mustansar Ali Ghazanfar and Adam Prugel-Bennett School of Electronics and Computer Science University of Southampton. A Scalable, Accurate Hybrid Recommender System. 2010 Third International Conference on Knowledge Discovery and Data Mining.

[5]Khushbu Jalan and Prof. Kiran Gawande. Context-Aware Hotel Recommendation System based on Hybrid Approach to Mitigate Cold-Start-Problem. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017).

[6]Sutheera Puntheeranurak and Hidekazu Tsuji. A Multi-Clustering Hybrid Recommender System. Seventh International Conference on Computer and Information Technology. © 2007 IEEE conference. Sumedh Sawant, Gina Pai. Yelp Food Recommendation System.

[7]Yashvardhan Sharma, Jigar Bhatt, Rachit Magon A Multi Criteria Review-Based Hotel Recommendation System. 2015 IEEE International Conference on Computer and Information Technology, Ubiquitous Computing Communications Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[8]Yannan Song, Shi Liu, Wei Ji. Research on Personalized Hybrid Recommendation System. Published in: 2017    international conference on computer, Information and Telecommunications System (CITS).

[9]Lipi Shah, Hetal Gaudani and Prem Balani. Survey on Recommendation System. International Journal of Computer Applications Volume 137 March 2016.

[10]Yao Xiao, Quan Shi. Research and Implementation of Hybrid Recommendation Algorithm Based on Collaborative Filtering and Word2Vec. 2015 8th International Symposium on Computational Intelligence and Design.

[11]James Huang, Stephanie Rogers, Eunkwang Joo. Improving Restaurants by Extracting Subtopics from Yelp Reviews. In Conference 2014 (Social Media Expo).

[12]Ya-han hu, ju lee, kuanchin chen, j. michael tarn, duyen-vi dang. hotel recommendation system based on review and context information: a collaborative filtering appro. (2016). pacis 2016 proceedings. 

[13]Richa Sharma, Sharu Vinayak, Rahul Singh,"Guide Me: A Research Work Area Recommender System", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.9, pp.30-37, 2016. DOI: 10.5815/ijisa.2016.09.04 .

[14]Santosh Kumar, Varsha," Survey on Personalized Web Recommender System", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.4, pp. 33-40, 2018. DOI: 10.5815/ijieeb.2018.04.05.

[15]Thoufeeq Ahmed Syed , Vasile Palade , Rahat Iqbal and Smitha Sunil Kumaran Nair. A Personalized Learning Recommendation System Architecture for Learning Management System. In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. 2017 MECS.