From a “Cold” to a “Warm” Start in Recommender Systems

Published in: 2014 IEEE 23rd International WETICE Conference

Date of Conference: 23-25 June 2014

INSPEC Accession Number: 14686576

Date Added to IEEE Xplore20 October 2014

DOI: 10.1109/WETICE.2014.6

Electronic ISBN:978-1-4799-4249-7

Publisher: IEEE

Print ISSN: 1524-4547

Conference Location: Parma, Italy


Chamsi Abu Quba Rana

Universite Claude Bernard Lyon 1, Villeurbanne, Auvergne-Rhône-Alpes, FR

Hassas Salima

Chairman & CTO, BlueKangaroo (ChoozOn Corp), United-States

Fayyad Usama

Université Lyon 1, Lyon, France

Chamsi Hammam

Université Lyon 1, Lyon, France


Human is surrounded by a tremendous and scary 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). In this work we highlight how iSoNTRE (the intelligent Social Network Transformer into Recommendation Engine) solves this problem by using users’ information in online social networks to overcome the cold start problem on new users, as well as iSoNTRE uses conceptual similarity, this overcomes the problem on new items, and on short life resources also. The work has been evaluated on Twitter on real users and results show that iSoNTRE succeeded in recommending offers to users with 14% of open rate on recommended offers, which is high compared to general open rate in social media, especially when we have nothing about users or offers before.

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