Metadata based recommender systems

For building a recommendation system the eCommerce portal gathers the user's ratings on various items in order to determine his/her choice regarding its merchandise. The portal also collects metadata for the user when he/she signs up and becomes a part of the system; therefore the portal has access to information such as user's age, gender, occupation, location, etc. Till date almost all prior studies used the metadata for alleviating the cold-start problem; this information was not used for improving the recommendations. For the first time in this work, we propose a simple neighborhood selection technique by giving importance to the metadata groups for improving the recommendations.