Ubiquitous User Modeling in Recommender systems Shlomo berkovsk Computer Science Department, University of Haifa, Israel slavax@cs. haifa. ac. il Abstract. The existing personalization services usually base on proprietary and partial user models. This work attempts at evolving inference-based mediation mechanism that will facilitate integrating user models coming from different sources, such as repositories of other service providers and users personal de- vices. This will allow obtaining more information about the users and providing more accurate personalization. The efficiency of the above approach will be demonstrated using the techniques from Recommender Systems domain I Better Personalization with Ubiquitous User Modeling Nowadays, the quantity of the available information rapidly grows and exceeds our limited processing capabilities. This is regarded in the literature as the Information Overload problem [5]. As a result there is a pressing need for intelligent systems that provide services according to user's personal needs and interests, and deliver tailored information in a way that will be most appropriate and valuable to the user. The state of-the-art personalization techniques basically overcome the Information Overload by tering the irrelevant information reaching the user. An essential input for every personalization technique is the model of the user [1] that is either collected by the service providers(through accumulating the information on user's preferences and interests), or imported into the system from user's personal devices(e.g, PDA, mobile phone, or personal media). For example, user's reading preferences might be stored by Amazon and BarnesAndNoble websites, and also by user's reading device. Thus, in the rest of this paper the term data source refers to the repositories of service providers, and of users' devices. ypically, the models stored by the repositories of service providers are proprietary and partial, as they fit a specific application and are limited to its domain. Since the level of personalization a system presents depends on the detailing of the input user models, different systems would improve the provided services by sharing the models tories to the commercial competition service pro- viders neither cooperate, nor share the data stored in their repositories A natural way of resolving this issue might be replicating the interactions between users and service providers also at the user side and directly accessing the models stored by users' personal devices. Hence, part of the user model and other personaliza tion information will be obtained from the collaborating users, and combined locally by the service provider that needs it. In addition to resolving the problem of non- cooperative service providers, direct interaction between different data sources in user modeling will partially resolve privacy concerns [4] L. Ardissono, P. Brna, and A. Mitrovic(Eds ) UM 2005, LNAI 3538, pp 496-498, 2005 e Springer-Verlag Berlin Heidelberg 2005L. Ardissono, P. Brna, and A. Mitrovic (Eds.): UM 2005, LNAI 3538, pp. 496 – 498, 2005. © Springer-Verlag Berlin Heidelberg 2005 Ubiquitous User Modeling in Recommender Systems Shlomo Berkovsky Computer Science Department, University of Haifa, Israel slavax@cs.haifa.ac.il Abstract. The existing personalization services usually base on proprietary and partial user models. This work attempts at evolving inference-based mediation mechanism that will facilitate integrating user models coming from different sources, such as repositories of other service providers and user's personal de￾vices. This will allow obtaining more information about the users and providing more accurate personalization. The efficiency of the above approach will be demonstrated using the techniques from Recommender Systems domain. 1 Better Personalization with Ubiquitous User Modeling Nowadays, the quantity of the available information rapidly grows and exceeds our limited processing capabilities. This is regarded in the literature as the 'Information Overload' problem [5]. As a result, there is a pressing need for intelligent systems that provide services according to user's personal needs and interests, and deliver tailored information in a way that will be most appropriate and valuable to the user. The state￾of-the-art personalization techniques basically overcome the Information Overload by filtering the irrelevant information reaching the user. An essential input for every personalization technique is the model of the user [1] that is either collected by the service providers (through accumulating the information on user's preferences and interests), or imported into the system from user's personal devices (e.g., PDA, mobile phone, or personal media). For example, user's reading preferences might be stored by Amazon and BarnesAndNoble websites, and also by user's reading device. Thus, in the rest of this paper the term 'data source' refers to the repositories of service providers, and of users' devices. Typically, the models stored by the repositories of service providers are proprietary and partial, as they fit a specific application and are limited to its domain. Since the level of personalization a system presents depends on the detailing of the input user models, different systems would improve the provided services by sharing the models stored in their repositories. However, due to the commercial competition service pro￾viders neither cooperate, nor share the data stored in their repositories. A natural way of resolving this issue might be replicating the interactions between users and service providers also at the user side and directly accessing the models stored by users' personal devices. Hence, part of the user model and other personaliza￾tion information will be obtained from the collaborating users, and combined locally by the service provider that needs it. In addition to resolving the problem of non￾cooperative service providers, direct interaction between different data sources in user modeling will partially resolve privacy concerns [4]