A flexible framework for context-aware recommendations in the social commerce domain

Authors:
Davide Ronca;Armando Calvanese;Cosimo Birtolo
Affiliations:
Poste Italiane Information Technology, Napoli - Italy;Poste Italiane Information Technology, Napoli - Italy;Poste Italiane Information Technology, Napoli - Italy
Venue:
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Year:
2013

Citing 9
Cited 0

Explaining collaborative filtering recommendations

CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Amazon.com Recommendations: Item-to-Item Collaborative Filtering

IEEE Internet Computing
Evaluating collaborative filtering recommender systems

ACM Transactions on Information Systems (TOIS)
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Valuating Privacy

IEEE Security and Privacy
Trust-aware recommender systems

Proceedings of the 2007 ACM conference on Recommender systems
Hybrid web recommender systems

The adaptive web
Incorporating social networks and user opinions for collaborative recommendation: local trust network based method

Proceedings of the Workshop on Context-Aware Movie Recommendation
A strategy-oriented operation module for recommender systems in E-commerce

Computers and Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Identifying the products to suggest to the target customer in order to best fit his profile of interests is a challenging task in the Social Commerce domain. In this paper we investigate the application of different model-based Collaborative Filtering (CF) techniques and we propose a framework able to provide different suggestions to the customers. The framework is aimed at expanding the customer suggestions in order to guarantee a serendipitous discovery of wished products and at the same time, it is aimed at assisting merchants in discovering potential interests of their customers. We compare several approaches by means of Epinions, MovieLens and Poste Italiane dataset (with real customers). Experimental results show an increased value of coverage of the recommendations provided by our approach without affecting recommendation quality. Moreover, personalized strategy guarantees a high level recommendation for different user navigation behaviors.