Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions

Emelia Opoku Aboagye and Rajesh Kumar
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Emelia Opoku Aboagye: Computer Science Department, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
Rajesh Kumar: Computer Science Department, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China

Future Internet, 2019, vol. 11, issue 1, 1-16

Abstract: We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making.

Keywords: multi-task framework; tensor factorization; sensors; pre-training; feature engineering; collaborative recommendation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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