Published in: 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)
Date of Conference: 10-13 November 2014
Date Added to IEEE Xplore: 02 April 2015
DOI: 10.1109/AICCSA.2014.7073195
Electronic ISBN: 978-1-4799-7100-8
Conference Location: Doha, Qatar
Authors:
Rana Chamsi Abu Quba
Universite Claude Bernard Lyon 1, Villeurbanne, Auvergne-Rhône-Alpes, FR
Salima Hassas
Polytech Lyon (University of Claude Bernard Lyon 1), France
Usama Fayyad
University of Michigan, Ann Arbor, MI, US
Milad Alshomary
OpenSooq, Saudi Arabia
Christine Gertosio
Universite Claude Bernard Lyon 1, Villeurbanne, Auvergne-Rhône-Alpes, FR
Abstract:
Human is surrounded by a tremendous amount of information on the web. That highlights the continuous need of recommendation systems in the different domains. Unfortunately cold start problem is still an important issue in these systems on new users and new items. The problem becomes more critical in systems that contain resources that lives too shortly like offers on products which stays only for few days (short life resources – SLiR), or news in a news site. From the other side social networks are very rich with users’ information, unfortunately most of the proposed social recommender are applied on domain specific social networks like flickers and epinions which are much less used in the day to day life, because dealing with General Purpose Social Network (GPSN) like Facebook and Twitter needs to transform these GPSN into a useful source of recommendation dealing with them as row, implicit or unary data. In this work we highlight how iSoNTRE (the intelligent Social Network Transformer into Recommendation Engine) addresses this challenge by transforming the GPSN into useful information for recommendation based on middle layer of domain concepts. iSoNTRE overcomes the cold start problem on new users and items. It has been evaluated over Twitter, on new users, recommending offers as a kind of SLiR, results showed that iSoNTRE succeeded in recommending good offers with 14% of click on recommended offers, which is high compared to general open rate in social media, especially when we have nothing about users and we are recommending SLiR resources.
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