Movie Recommendation System: Content-Based and Collaborative Filtering - Raghuwanshi, SK; Pateriya, RK (International Journal of Computer Sciences and Engineering, 2025)
Since last decade a huge amount of information is transferred over the internet on day to day basis. However, all the information is not relevant to each user and is also difficult to find the right content for the user as per his/her need. Recommender system works as a guide to find or suggest right items for users. A movie recommendation system is predicting or suggest a movie which user might like using his/her previous watch list or history. After Netflix prize competition many academician and researchers have shown interest to develop new and better filtering techniques for the movie recommendation. This paper studies the two most fundamental techniques: content-based and collaborative filtering methods of information retrieval and shows their application for movie recommendation with pros and cons. An experiment was carried out over MovieLens 100K dataset to show the implementation of discussed methods. The obtained results have shown that Item-Item based neighbourhood collaborative filtering method is better among implemented three techniques with 0.786 MAE and 0.985 RMSE values.