Holistic User Context-Aware Recommender Algorithm

Tatenda D. Kavu, Kudakwashe Dube and Peter G. Raeth

Mathematical Problems in Engineering, 2019, vol. 2019, 1-15

Abstract:

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3965845

DOI: 10.1155/2019/3965845

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