A personalised movie recommendation system based on collaborative filtering

TLDR

The explosion of digital data from social media, e-commerce, and enterprise digitisation has driven the widespread adoption of recommendation systems to inform choices and predict consumer preferences. The study aims to use filtering and clustering techniques to recommend items of interest to users. The system recommends movies by identifying users with similar tastes, collecting initial ratings, and refining suggestions as users interact. Experiments on MovieLens show that the model delivers precise, more personalized recommendations than competing approaches.

Abstract

Over the last decade, there has been a burgeoning of data due to social media, e-commerce and overall digitisation of enterprises. The data is exploited to make informed choices, predict marketplace trends and patterns in consumer preferences. Recommendation systems have become ubiquitous after the penetration of internet services among the masses. The idea is to make use of filtering and clustering techniques to suggest items of interest to users. For a media commodity like movies, suggestions are made to users by finding user profiles of individuals with similar tastes. Initially, user preference is obtained by letting them rate movies of their choice. Upon usage, the recommender system will be able to understand the user better and suggest movies that are more likely to be rated higher. The experiment results on the MovieLens dataset provides a reliable model which is precise and generates more personalised movie recommendations compared to other models.