Transfer Learning for Resolving Sparsity Problem in Recommender systems: Human Values Approach

With the rapid rise in popularity of ecommerce application, Recommender Systems are being widely used by them to predict the response that a user will give to a given item. This prediction helps in cross selling, upselling and to increase the loyalty of their customers. However due to lack of suffic...

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Bibliographic Details
Published in:Revista de gestão da tecnologia e sistemas de informação 2017-09, Vol.14 (3), p.323-337
Main Authors: Srivastava, Abhishek, Bala, Pradip Kumar, Kumar, Bipul
Format: Article
Language:English
Subjects:
Algorithms
Basic Human Values
Collaboration
Collaborative filtering
COMPUTER SCIENCE, INFORMATION SYSTEMS
Efficiency
Human behavior
Learning
Personality
Ratings & rankings
Recommender systems
Social networks
Sparsity
Sparsity problem
Transfer learning
User profiles
Values
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Summary:With the rapid rise in popularity of ecommerce application, Recommender Systems are being widely used by them to predict the response that a user will give to a given item. This prediction helps in cross selling, upselling and to increase the loyalty of their customers. However due to lack of sufficient feedback data these systems suffer from sparsity problem which leads to decline in their prediction efficiency. In this work, we have proposed and empirically demonstrated how the Transfer Learning approach using five dimensions of basic human values can be successfully used to alleviate the sparsity problem and increase the efficiency of recommender system algorithms.
ISSN:1807-1775
1809-2640
1807-1775
DOI:10.4301/S1807-17752017000300002