Movie Recommendation System Based on Sentence Transformers: Personalized Recommendations Driven by LLM Semantic Understanding

Title: Movie Recommendation System Based on Sentence Transformers: Personalized Recommendations Driven by LLM Semantic Understanding

Project Source: Published by aalvarez359 on GitHub (Link: https://github.com/aalvarez359/movie_recommend_llm, Release Date: 2026-05-23). The core is to build a movie recommendation system using Sentence Transformers technology, which captures deep correlations between user preferences and movie content through semantic embeddings, solving problems like cold start and sparsity in traditional collaborative filtering to achieve more precise personalized recommendations.

Evolution of Recommendation Systems and LLM Intervention

Movie recommendation systems have evolved through rule-based recommendation → collaborative filtering → deep learning. Traditional collaborative filtering relies on user-item interaction matrices but has limitations such as cold start (no data for new users/movies), sparsity (few user ratings), and inability to capture semantic information.

The rise of pre-trained models like LLMs and Sentence Transformers can convert text into dense semantic vectors, bringing new possibilities for content-based recommendations and improving accuracy.

Core Technical Architecture

Introduction to Sentence Transformers

Sentence Transformers is an extension of SBERT that generates sentence-level semantic embeddings. Its features include semantic similarity calculation, pre-trained model ecosystem, efficient inference, and multi-language support.

Movie Content Vectorization

Process: Text preprocessing (cleaning and standardization) → multi-field fusion (combining title, synopsis, genre, etc.) → embedding generation (converting to fixed-dimensional vectors; similar movies have close vector distances).

User Preference Modeling

Integrate explicit feedback (weighted average of highly rated movies) and implicit signals (browsing duration, favorites, etc.) to dynamically update user preference vectors.

Recommendation Algorithm Implementation

Vector Similarity Retrieval

Cosine similarity is used to calculate the similarity between user vectors and movie vectors. Its advantage is focusing on direction rather than magnitude, avoiding length bias.

Approximate Nearest Neighbor (ANN) Search

ANN algorithms like FAISS/Annoy are used to reduce retrieval complexity (O(logN)), balancing recall rate and speed.

Diversity Balance

Balance relevance and diversity via MMR, balance exploration and exploitation to recommend new genres, use time decay to increase exposure of new movies, and avoid filter bubbles.

Comparison of Technical Advantages

Comparison with Traditional Collaborative Filtering

Dimension	Traditional Collaborative Filtering	LLM+Sentence Transformers
Cold Start Handling	Difficult	Good
Interpretability	Weak	Strong
Semantic Understanding	None	Deep Understanding
Cross-domain Transfer	Difficult	Easy

Comparison with Keyword Matching

Traditional keyword matching only considers literal similarity, while semantic embeddings can identify theme similarity (e.g., Interstellar and 2001: A Space Odyssey), avoiding missed recommendations.

Application Scenarios and Extensibility

Multimodal Fusion

Can integrate models like CLIP to fuse visual features of movie posters/stills, enriching content understanding.

Cross-domain Transfer

Model knowledge can be transferred to recommendation scenarios like books and music, expanding application value.

Real-time Personalized Streams

Combine stream processing frameworks like Kafka/Flink to capture user behavior in real time and update recommendations instantly.

Challenges and Optimization Directions

Semantic Drift

Movie language evolves over time; regular model fine-tuning or time-aware embedding updates are needed.

Long-tail Content Coverage

Descriptions of niche movies are incomplete; knowledge graph completion and cross-language aggregation can be used to enhance representations.

Computational Resource Optimization

Use techniques like product quantization to compress indexes, reduce memory usage, and suit deployment in resource-constrained environments.

Summary and Outlook

This project combines LLM semantic understanding with a recommendation architecture to solve pain points of traditional recommendations. Sentence Transformers enable the system to understand the 'meaning' of content rather than just keywords.

In the future, multimodal large models will support multi-dimensional understanding of text, images, etc., providing a comprehensive personalized experience. This open-source project provides a technical foundation and architectural reference for this vision.