In this paper we are proposing a design of TV program and settings recommendation engine utilizing contextual parameters like personal, social, temporal, mood and activity. In addition to the contextual parameters the system utilize the explicit or implicit user ratings and watching history to resolve the conflict if any while recommending the services .The System is implemented exploiting AI techniques ( like ontology, fuzzy logic ,Bayesian classifier and Rule Base) , RDBMS and SQL Query Processing . The motivation behind the proposed work is i) to improve the user's satisfaction level and ii) to improve the social relationship between user and TV. The context aware recommender utilizes social context data as an additional input to the recommendation task alongside information of users and tv programs. We have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system for small families.

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In this paper we are proposing a GUI based Prototype for user centered environment like class room, library hall, laboratory, meeting hall, coffee shop, kitchen, living room and bedroom, which recommends useful services based on the user’s context. Service recommendation is mainly based on parameters such as user, location, time, day and mood. Inaddition whenever the conflict arises among different users it will be resolved using some conflict resolving algorithms. The motivation behind the proposed work is to improve the user satisfaction level and to improve the social relationship between user and devices The prototype contains simulated sensors which are used to capture the raw context information, which is then described with meaningful English sentence and services are recommended based on user’s situation. The proposed conflict resolving algorithms are Rule based algorithm, Bayesian probability based algorithm and Rough set theory based algorithm. The amount of conflicts resolved by these algorithms is also analyzed at the end.