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Published in:
2018 | OriginalPaper | Chapter
An Experimental Study of Scalability in Cross-Domain Recommendation Systems
Authors : Akarsh Srivastava, Aman Jain, Ashwin Jayadev, Rajdeep Mukherjee, Shronit Bhargava, Prosenjit Gupta
Published in: Advanced Computational and Communication Paradigms
Publisher: Springer Singapore
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Abstract
Recommender systems attempt to predict the future behavior of a particular individual based on her past preferences. Today any individual may have more than one profile that he/she maintains on various websites, and leveraging all this data on the preference of an individual from various domains (cross-domain) can help us in making better user models that can be used to make better and improved recommendation. A cross-domain recommender system thus aims to improve the recommendation of a target domain extracting and using the metadata from many source domains. Building scalable recommender systems is always a challenge in the presence of Big Data, and this is compounded for cross-domain recommenders. In this paper, we aim to tackle the problem of scalability in cross-domain recommendations. We have performed various experiments to divide the datasets into smaller clusters and then running a recommendation algorithm using the attributes in the dataset to return the best recommendations.