Sequence Recommendation Using Movie Lens Dataset in Python Projects

Sequence-Based Movie Recommendation System Using MovieLens Dataset and Deep Learning

Abstract

The Sequence Recommendation System is an intelligent movie recommendation application developed using Python, Machine Learning, Deep Learning, and Flask. The system predicts the next movie a user is likely to watch based on their historical viewing sequence rather than only their ratings. Using the MovieLens Dataset, sequential models such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit) learn user viewing patterns to generate personalized recommendations. The Flask web application provides an interactive interface where users can log in, view recommendations, search movies, and receive personalized suggestions based on their watch history.

Existing System

Traditional recommendation systems mainly rely on:

Collaborative Filtering
Content-Based Filtering
Matrix Factorization
Popularity-Based Recommendation
Disadvantages
Ignores viewing order.
Cold-start problem.
Limited personalization.
Difficulty adapting to changing user preferences.
Lower recommendation accuracy for sequential behavior.
Proposed System

The proposed system employs Sequence Recommendation using LSTM/GRU to model users' viewing histories.

Features
User login and profile.
Movie search.
Viewing history tracking.
Sequence-based recommendation.
Personalized movie suggestions.
Top-N recommendations.
Admin dashboard.
Recommendation history.
Objectives
Recommend the next movie based on viewing sequence.
Improve recommendation accuracy.
Learn changing user preferences.
Handle long-term user behavior.
Provide personalized recommendations.

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