Next Generation of Recommender Systems: Algorithms and Applications

Li, Lei

doi:10.25148/etd.fi14071110

Cited by 1 publication

(1 citation statement)

References 158 publications

(74 reference statements)

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“…Contextual information/features of a user such as time and location are quite important in making novel and serendipitous recommendations [1,7,13,14]. Modelling the contextual information about a user enables the recommendation engine to provide more reasonable recommendations for users [13,15], and context adaptation is one major goal of session-aware recommendation engines [16].…”

Section: Related Workmentioning

confidence: 99%

Holistic User Context-Aware Recommender Algorithm

Kavu

Dube

Raeth

2019

Mathematical Problems in Engineering

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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.

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