Web has a tremendous growth in terms of both content and number of users, this has led to a serious problem of information overloading in which it is difficult for users to locate authentic information in the given time. Recommender Engines have been developed to address this problem, by guiding the users through the information and helping them find the right information. Traditional Recommender Engine sought to predict the 'rating' or 'preference' that a user would give to an item or social element they had not yet considered, this model is developed from the characteristics of an item or the user's social environment. Spatially Aware Recommender Engine on the other hand produces a location-aware recommender system that uses location based ratings to produce recommendations. This project will present the design, implementation, testing and evaluation of a recommender system with the solution for Limited resource situation and cold start problem using Hybrid filtering algorithm, Lesk based algorithm and Random algorithm.

Recommendation system limited resource situation cold start problem