Combination of a Cluster-Based and Content-Based Collaborative Filtering Approach for Recommender System

Abstract

With the development in technology in the field of e-commerce, the problem with information overload has been at its peak. Oftentimes the user is overwhelmed by the huge amount of options he/she is provided with while searching for an item. This is when recommender system comes in handy, which is an information filtering technique aimed at presenting the user with the most possible options based on certain reference characteristics. However, the problem with many recommender systems is that they are associated with a high cost of learning customer preferences. The current agricultural web application uses recommendation system along with the collaborative filtering concept which introduces the Agricultural Informative System (AIS) that uses pseudo feedback, which provides a method for automatic local analysis about the user preferences with the help of clustering in collaborative filtering. The AIS uses pseudo feedback to capture the preferences which are stored in the users profile for future personalized recommendations to address the problem.
DOI: 10.17762/ijritcc2321-8169.150784