Multi-Algorithmic Techniques and a Hybrid Model for Increasing the Efficiency of Recommender Systems

Abstract

The explosive growth in the amount of available digital information has increased the demand for recommender systems. Recommender systems are information filtering systems that deal with the problem of information overload by filtering vital information fragment out of large amount of dynamically generated information according to user's preferences or interests. Recommender systems have the ability to predict whether a particular user would prefer an item or not based on his/her personal profile. To this direction, this paper presents multi-algorithmic techniques, such as content-based filtering and collaborative filtering, which increase the efficiency of recommender systems. Moreover, a hybrid model for recommendation, employing content-based and collaborative filtering, is introduced. The presented recommender system takes as input information about users from their profile in Facebook, one of the most well-known social networking services. Examples of operation are given and they hold promising results for the described techniques. Finally, the paper attests that the aforementioned techniques can be used for different kind of software, such as e-learning, e-commerce, etc.