Title: Diversification-oriented accuracy prediction in recommender systems

Authors: P. Valarmathi; R. Dhanalakshmi; Narendran Rajagopalan; Bam Bahadur Sinha

Addresses: National Institute of Technology Puducherry, Karaikal, Puducherry 609609, India ' Department of CSE, Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli 620009, India ' Department of CSE, National Institute of Technology Puducherry, Karaikal, Puducherry 609609, India ' Indian Institute of Information Technology Dharwad, Karnataka 580029, India

Abstract: Tremendous amount of data generated by e-commerce users on items (e.g., purchase or rating history), sets some key challenges for the online knowledge discovery scheme. Recommendation systems are an important element of the digital marketplace such as e-stores and service providers that use the generated information to discover preferred products of the consumers. Developing an effective recommender system that produces diverse suggestions without compromising the precision of the customised list is challenging for online systems. This paper aims at diversifying recommendation by integrating graph-based algorithm supported with significant nearest neighbour strategy for enhancing recommendation precision. The experimental efficacy on the 100K dataset of MovieLens shows that the proposed hybrid model has a strong coverage and superior efficiency in product recommendations.

Keywords: e-commerce; significant nearest neighbour; SNN; graph-based algorithm; GBA; diversification; coverage; MovieLens.

DOI: 10.1504/IJISE.2022.123583

International Journal of Industrial and Systems Engineering, 2022 Vol.41 No.2, pp.206 - 220

Received: 22 May 2020
Accepted: 09 Aug 2020
Published online: 28 Jun 2022 *

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