Title: Context-aware adaptive personalised recommendation: a meta-hybrid

Authors: Peter Tibensky; Michal Kompan

Addresses: Slovak University of Technology, Ilkovicova 2, Bratislava, Slovakia ' Kempelen Institute of Intelligent Technologies, Mlynske Nivy 5, Bratislava, Slovakia

Abstract: Recommenders take place on a wide scale of e-commerce systems, reducing the problem of information overload. The most common approach is to choose a recommender used by the system to make predictions. However, users vary from each other; thus, a one-fits-all approach seems to be sub-optimal. In this paper, we propose a meta-hybrid recommender that uses machine learning to predict an optimal algorithm. In this way, the best-performing recommender is used for each specific session and user. This selection depends on contextual and preferential information collected about the user. We use standard MovieLens and The Movie DB datasets for offline evaluation. We show that based on the proposed model, it is possible to predict which recommender will provide the most precise recommendations to a user. The theoretical performance of our meta-hybrid outperforms separate approaches by 20%-50% in normalised discounted gain and root mean square error metrics. However, it is hard to obtain the optimal performance based on widely-used standard information stored about users.

Keywords: personalised recommendation; hybrid recommender; context; machine learning.

DOI: 10.1504/IJWET.2021.119874

International Journal of Web Engineering and Technology, 2021 Vol.16 No.3, pp.235 - 254

Received: 12 Feb 2021
Accepted: 23 Aug 2021
Published online: 22 Dec 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article