The Future Directions of Recommender Systems

Authors: Mona Taghavi and Kaveh Bakhtiyari

Helping users handle the issue of information overload was perceived to be the original task of search engines or information retrieval systems, but what makes recommender systems distinct from search engines are the criteria of being “personalized, interesting and useful”. In fact, when a user is using a search engine, she knows what she is looking for, and makes the query accordingly. In contrast, recommender systems operate when the user does not know what she wants or likes, but the system knows the users’ tastes; finds items that she prefers.

What makes a recommendation more interesting and useful is the factor of “intelligence”. Intelligence is the key core of personalization to understand the user’s preferences, predict user’s unknown favorites, and at the end provide recommendations beyond a simple search by matching the query and the content. Recommender systems research has incorporated a wide variety of Artificial Intelligence (AI) techniques including machine learning, data mining, user modeling, and case-based reasoning, among others. The idea of having an intelligent system, which can think and learn like a human, led to more humanized techniques called Computational Intelligence (CI). CI is a branch of AI that explores the adaptive mechanisms to enable intelligent actions within the…