Title: Constraint-based feature selection for location intelligent data mining.

Abstract:
Recommender Systems (RS) are software systems which, are designed to aid the users. They are based on the observation that, in the real world, people heavily rely on the recommendations of others when making daily decisions. Such decisions are, for example,    items to buy, what movies to see, and so forth.  Recommender Systems thus involve the design of algorithms which seek to mimic this behavior.  Recommender systems may produce non-personalized recommendations, such as best sellers’ lists; or personalized recommendations, based on person’s likes, preferences and behaviors.
The new trend of system development is the creation of Recommender Systems which provide personalized services to each user. Unfortunately, the current approaches base their results on a single criterion or pattern, and require a large number of user’s data to generate accurate recommendations. Our research focuses on the creation of a multiple criteria RS, which is tailored for a specific user at a specific location. Our algorithm creates a personalized preference model for each user, based on location aware (user current location and level of mobility), special needs, and user profile. User profiles are generalized from a large scale demographic database. We describe our algorithm and show a case study where we applied our methodology within the PeRS (Personal Recommender System) environment to recommend “events” (festivals/fairs, exhibitions, etc), in the Ottawa region.