Title: STEM: stacked ensemble model design for aggregation technique in group recommendation system

Authors: Nagarajan Kumar; P. Arun Raj Kumar

Addresses: Department of Computer Science and Engineering, National Institute of Technology (NIT) Calicut, NIT Campus P.O., Kozhikode, Kerala State – 673601, India ' Department of Computer Science and Engineering, National Institute of Technology (NIT) Calicut, NIT Campus P.O., Kozhikode, Kerala State – 673601, India

Abstract: A group recommendation system is required to provide a list of recommended items to a group of users. The challenge lies in aggregating the preferences of all members in a group to provide well-suited suggestions. In this paper, we propose an aggregation technique using stacked ensemble model (STEM). STEM involves two stages. In stage 1, the k-nearest neighbour (k-NN), singular value decomposition (SVD), and a combination of user-based and item-based collaborative filtering is used as base learners. In the second stage, the decision trees predictive model is used to aggregate the outputs obtained from the base learners by prioritising the most preferred items. From the experiments, it is evident that STEM provides a better group recommendation strategy than the existing techniques.

Keywords: group recommendation system; aggregating user preferences; decision trees; stacked ensemble; machine learning.

DOI: 10.1504/IJBIDM.2022.123809

International Journal of Business Intelligence and Data Mining, 2022 Vol.21 No.1, pp.66 - 84

Received: 07 Jul 2020
Accepted: 18 Dec 2020
Published online: 04 Jul 2022 *

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