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

Rana Chamsi Abu Quba Salima Hassas 1 Hammam Chamsi 2 Usama Fayyad
1 SyCoSMA - Systèmes Cognitifs et Systèmes Multi-Agents
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : 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 we called this resources the 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 the online social networks to overcome the cold start problem on new users’, as well as iSoNTRE uses the 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 user positive feedback, where we had nothing about users or offers before.
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Submitted on : Monday, April 11, 2016 - 4:28:57 PM
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Rana Chamsi Abu Quba, Salima Hassas, Hammam Chamsi, Usama Fayyad. From a “Cold” to a “Warm” Start in Recommender systems. IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2014), Jun 2014, Parma, Italy. pp.1-7, ⟨10.1109/WETICE.2014.66⟩. ⟨hal-01301058⟩



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