Context-aware adaptive personalised recommendation: a meta-hybrid

TL;DR

This paper introduces a meta-hybrid recommender system that uses machine learning to select the most suitable algorithm for each user session, improving recommendation accuracy by 20-50% over traditional methods.

Contribution

The paper presents a novel meta-hybrid approach that dynamically predicts the best recommender for each user session based on contextual and preference data.

Findings

Meta-hybrid model outperforms individual recommenders by 20-50% in accuracy metrics.

The approach effectively predicts the most precise recommender for each user session.

Standard user data makes it challenging to achieve optimal performance.

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 normalized 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.