Publication: An Efficient and Scalable Recommender System for the Smart Web
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2015-11-01
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IEEE. Computer Society
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Abstract
This work describes the development of a web recommender system implementing both collaborative filtering and content-based filtering. Moreover, it supports two different working modes, either sponsored or related, depending on whether websites are to be recommended based on a list of ongoing ad campaigns or in the user preferences. Novel recommendation algorithms are proposed and implemented, which fully rely on set operations such as union and intersection in order to compute the set of recommendations to be provided to end users. The recommender system is deployed over a real-time big data architecture designed to work with Apache Hadoop ecosystem, thus supporting horizontal scalability, and is able to provide recommendations as a service by means of a RESTful API. The performance of the recommender is measured, resulting in the system being able to provide dozens of recommendations in few milliseconds in a single-node cluster setup.
Description
This proceeding at: 11th International Conference on Innovations in Information Technology (IIT) Innovations 2015. Special Theme: Smart Cities, Big Data, Sustainable Development. Took place at 2015, November, 01 - 03, in Dubai, United Arab Emirates (IEEE IIT 2015).
Keywords
Algorithm design and analysis, Big data, Collaboration, Computer architecture, Real-time systems, Recommender systems, Amart Cities, Uniform resource locators.
Bibliographic citation
Proceedings of the 2015. 11th International Conference on Innovations in Information Technology (IIT). Innovations 2015. Special Theme: Smart Cities, Big Data, Sustainable Development, pp. 296-301.