Hybrid group recommendation using modified termite colony algorithm: A context towards big data

Arup Roy, Soumya Banerjee, Chintan Bhatt, Youakim Badr, Saurav Mallik

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Article number1850019
JournalJournal of Information and Knowledge Management
Volume17
Issue number2
DOIs
StatePublished - Jun 1 2018

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Library and Information Sciences

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