Contextually Intelligent Recommendation of Documents from User-Subscribed Channels

There are several professional, academic and business channels catering to the information needs of professionals. In this paper we have presented a contextual recommendation system that gathers documents from these channels on behalf of a user and recommends the most relevant documents from the collection based on the current work-context of the user. Starting with an initial context, the system employs reinforcement learning to understand user interests and then recommend contextually relevant articles only and thereby reduce information overload. The paper presents a theoretical framework for context detection and contextual recommendation and some experimental results from simulated environments.