A System based on Multiple Criteria Analysis for Scientific Paper Recommendation

Systems able to suggest items that a user may be interested in are usually named as Recommender Systems. The new emergent field of Recommender Systems has undoubtedly gained much interest in the research community. Although Recommender Systems work well in suggesting books, movies and items of general interest, many users express today a feeling that the existing systems don’t actually identify them as individual personalities. This dissatisfaction turned the research society towards the development of new approaches on Recommender Systems, more user-centric. A methodology originated from Decision Theory is exploited herein, aiming to address to the lack of personalization in Recommender Systems by integrating the user in the recommendation process.