Abstract

Recommender systems in e-commerce applications have become business relevant in filtering the vast range of information available in web shop (and the internet) to present useful recommendation to user. In this study we combine social network analysis and semantic user profile to provide a new semantic-social recommendation, featuring a two-stage process that relies on a simple formalization of semantic user preferences that contains the user's main interests and heuristically explores the social graph. Given a recommendation request concerning a product, the semantic-social recommendation algorithm compares the user preferences, which are found in the exploration path, with the product preferences by referencing them to domain ontology. Experiments on real-world data from Amazon, examine the quality of our recommendation method as well as the efficiency of our recommendation algorithms.

Keywords:

Recommender systems, semantic web, social network, taxonomy, user preferences,

References

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.