Holistic User Context-Aware Recommender Algorithm

Abstract EN

Existing recommender algorithms lack dynamism, human focus, and serendipitous recommendations.

The literature indicates that the context of a user influences user decisions, and when incorporated in recommender systems (RSs), novel and serendipitous recommendations can be realized.

This article shows that social, cultural, psychological, and economic contexts of a user influence user traits or decisions.

The article demonstrates a novel approach of incorporating holistic user context-aware knowledge in an algorithm to solve the highlighted problems.

Web content mining and collaborative filtering approaches were used to develop a holistic user context-aware (HUC) algorithm.

The algorithm was evaluated on a social network using online experimental evaluations.

The algorithm demonstrated dynamism, novelty, and serendipity with an average of 84% novelty and 85% serendipity.