Learning and inference engine applied to ubiquitous recommender system
Learning and inference engine applied toubiquitous recommender systemDjallel BouneffoufInstitut Telecom, Telecom SudParis, CNRS UMR Samovar9, rue Charles Fourier, 75004 EvryDjallel.email@example.comThe need for adapting information systems to the user context has been accentuatedby the extensive development of mobile applications that provide a considerableamount of data of all types (images, texts, sounds, videos, etc.). It becomes thuscrucial to help users by guiding them in their access to information.Systems should be able to recommend information helping the user to fulfill his/hergoal. The information given by the system depends on the user’s situation, i.e. aninstance of the context. Possible situations and the associated actions reflect theuser’s work habits.Major difficulties when applying techniques to adapt a system to the user follow:- Avoiding the intervention of experts: on one hand, experts are not sure of theinterest of the user, may define wrong ideas about him; on the other hand, an expertis not always available [9, 26].- Starting from scratch: in the initial state, the system’s behavior should not beincoherent for the user to not refuse it quickly [7, 27, 29].- A slow learning process: the learning process has to be quick to avoid botheringthe user with incorrect recommendation [11, 22, 23, 24]. -The evolution of the user’s interest: the interest of the user may change with thetime. The system has to be continuously adapted to this dynamic change using theuser’s context information to provide the relevant recommendations because, if thesystem behavior is incoherent, the user refuses it quickly [9, 19, 31].We sum up all of these problems in the following scenario.“Knowing the high mobility of its employees and their dependencies to theinformation contained in their corporate databases, the Nomalys company hasequipped all mobile phones with the “NS” application. This application allowsthem to adapt to the nomadic life by consulting the company’s database from theirmobile phones.Knowing the high mobility of its employees and their dependencies to theinformation contained in their corporate databases, the Nomalys company hasequipped all mobile phones with the “NS” application. This application allows themto adapt to the nomadic life by consulting the company’s database from their mobilephones. Because of the diversity of jobs existing in the company, Nomalys decidesprovide the application with a generic recommender system, which has to retrievethe relevant information to users without any set of actions predefined by an expert.
Paul, John and Lauren are new employees of the company integrating different teamsof the company (marketing, commercial, and technique resp.).Regarding their agendas, they have a meeting with clients in Paris at midday. Whenthey arrive at their meetings, the system should recommend them the relevantinformation which would help them to better manage their meetings. The systemrecommends to Paul the register of complaints, to John the register of factures and toLauren the technical registers.To do these recommendations without the need of an expert and avoiding startingfrom scratch, the recommender system has to infer them from the actions of theuser’s team.The recommender system finds that Paul often opens the register of complaints twohours before his meeting and not at the meeting. Moreover, John always tries to findcompanies which are near and do the same work as the one he will visit the next day.Using this knowledge, one month later, the system is able to recommend the registerof complaints to Paul two hours before his meeting; it also recommends Johncompanies which are near and do the same work as the one he will visit the nextday”.To do these recommendations, the learning process of the system has to be quick andhas to follow the evolution user’s interest.In summary, to solve the problem of the scenario, I study during my PHD thesis thepossibility to start with a predefined set of actions, not defined by an expert, but bythe user’s social group (in the scenario we talk about job teams) and adapts itprogressively to a particular user. This default behavior allows the system to beready-to-use and the learning is a lifelong process. Thus, the system will, at first, beonly acceptable to the user, and will, as time passes, give more and more satisfyingresults.8. Bibliographie[Assad et al., 2007] Mark Assad, David Carmichael, Judy Kay et Bob Kummerfeld. «PersonisAD : Distributed, Active, Scrutable Model Framework for Context-Aware Services ».Dans Anthony La-Marca, Marc Langheinrich et Khai N. Truong, rédacteurs, Proceedings ofthe 5th International Conference on Pervasive Computing, PERVASIVE 2007, tome 4480 deLecture Notes in Computer Science, pages 55 - 72. Springer, Toronto, Ontario, Canada, mai2007.[Godoy et Amandi, 2005] Daniela Godoy et Analia Amandi. « User proling for Web pagefiltering », Internet Computing, IEEE, tome 9, n° 4,pages 56-64, juillet - aout 2005.[Christopher, 2006] Christopher M. Bishop, « Pattern Recognition And Machine Learning»,Springer, 2006.