Comparing Content Based and Collaborative Filtering in Recommender Systems

Parul Aggarwal, Vishal Tomar, Aditya Kathuria

In daily life we need many things to be searched over the internet, for search purpose there are many search engines available. Whenever we search something we try to get the most relevant results, and this can be achieved using Recommender systems.In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. The paper presents an overview of the field of recommender systems and describes the difference between two of the most used approaches in recommender systems, i.e. Collaborative Filtering and Content based Filtering Techniques.