A distributed cross layer recommender system incorporating product diffusion. (14th February 2019)

• Record Type: Journal Article
• Authors: Thendral, S. Ephina; Valliyammai, C.
• Is Part Of: International journal of business intelligence and data mining. Volume 14:Number 3(2019)
• Page Start: 381
• Page End: 400
• Publication Date: 2019-02-14
• Subjects: recommender systems -- personalisation -- product diffusion -- distributed graph model; 006.312
• Languages: English
• ISSNs: 1743-8187

Abstract

In this era of online retailing, personalisation of web content has become very essential. Recommender system is a tool for extraction of relevant information to render personalisation in web information retrieval systems. With an inclination towards customer oriented service, there is a need to understand the adaptability of customers, to provide products/services of interest at the right time. In this paper, a model for distributed context aware cross layer recommender system incorporating the principle of product diffusion is proposed. The offline-online modelled recommender system learns offline about the adaptation time of users using the principle of product diffusion and then, uses online explore-then-exploit strategy to make effective recommendations to the user at the most probable time of consumption. Also, an algorithm based on product adaptability is proposed for recommending new items to the most probable users. The extensive experiments and results demonstrate the efficiency, scalability, reliability and enhanced retrieval effectiveness of the proposed recommender system model.