Addressing Cold-Start Problem In Movie Recommendation System Using Sequence Modeling_874

The recommendation system is a classification of machine learning [1] that uses features to help anticipate, compact and find what people are looking for an exponentially growing number of options. It is an artificial intelligence algorithm in machine learning, which uses big data to recommend more related items to consumers. These can be based on different criteria, which includes past purchase, search history, demographic information and other factors. These systems are designed to predict what a user might like based on various factors. They are extensively used in various domains including e-commerce, streaming services, social networks and content platforms. In this paper we have proposed a novel movie recommendation system that effectively addresses the cold start user problem by leveraging sequence modeling techniques [2]. Traditional recommendation systems struggle with new users due to the lack of historical interaction data. Our approach utilizes sequence modeling, specifically Long Short Term Memory (LSTM) networks, which predict user preferences based on initial interactions. By analyzing the sequence of the movie watched, our model can generate accurate recommendations even with minimal user data.