Context-aware adaptive personalised recommendation: a meta-hybrid
This addresses the need for personalized recommendations in e-commerce to reduce information overload, though it is incremental as it builds on existing hybrid methods.
The paper tackles the problem of sub-optimal one-size-fits-all recommendations by proposing a meta-hybrid recommender that predicts the best algorithm per user and session, showing theoretical performance gains of 20-50% in nDCG and RMSE metrics.
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.