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Hybrid Group Recommendation Using Modified Termite Colony Algorithm: A Context Towards Big Data

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- Erratum: "Hybrid Group Recommendation Using Modified Termite Colony Algorithm: A Context Towards Big Data"

Arup Roy

Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Jharkhand 814142, India

E-mail Address: aruproy.cse@gmail.com

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Soumya Banerjee

Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Jharkhand 814142, India

E-mail Address: soumyabanerjee@bitmesra.ac.in

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Chintan Bhatt

Department of Computer Science and Engineering, Charotar University of Science and Technology, Changa, Gujarat 388421, India

E-mail Address: chintanbhatt.ce@charusat.ac.in

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Youakim Badr

CNRS INSA De Lyon, LIRIS Lab, Lyon, UMR-5205, France

E-mail Address: youakim.badr@insa-lyon.fr

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, and

Saurav Mallik

Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal 700108, India

E-mail Address: sauravmtech2@gmail.com

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https://doi.org/10.1142/S0219649218500193Cited by:9 (Source: Crossref)

Abstract

Since the introduction of Web 2.0, group recommendation systems become an effective tool for consulting and recommending items according to the choices of group of likeminded users. However, the population of dataset consisting of the large number of choices increases the size of storage. As a result, identification of the combination for specific recommendation becomes complex. Hence, the existing group recommendation system should support methodology for handling large data volume with varsity. In this paper, we propose a content-boosted modified termite colony optimisation-based rating prediction algorithm (CMTRP) for group recommendation system. CMTRP employs a hybrid recommendation framework with respect to the big data paradigm to deal with the trend of large data. The framework utilises the communal ratings that help to overcome the scalability problem. The experimental results reveal that CMTRP provides less error in the rating prediction and higher recommendation precision compared with the existing algorithms.

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