# The movie recommendation algorithm based on the TransD model and AIGC empowerment and its application effectiveness analysis

## TL;DR

This paper introduces a new movie recommendation system using TransD and AIGC to better handle user preferences and cold start issues.

## Contribution

A novel framework combining TransD and AIGC for improved semantic modeling and user interest representation in recommendation systems.

## Key findings

- The proposed model outperforms traditional models in hit rates and user satisfaction across multiple datasets.
- The system shows superior stability and adaptability in cold start and interest transition scenarios.
- AIGC enhances semantic modeling by extracting latent interests and emotional characteristics from user reviews.

## Abstract

This study aims to enhance the recommendation system’s capability in addressing cold start issues, semantic understanding, and modeling the diversity of user interests. The study proposes a movie recommendation algorithm framework that integrates Knowledge Graph Embedding via Dynamic Mapping Matrix (TransD) and Artificial Intelligence Generated Content (AIGC)-based generative semantic modeling. This framework is designed to overcome existing challenges in recommendation algorithms, including insufficient user interest representation, inadequate knowledge graph relationship modeling, and limited diversity in recommended content. Traditional recommendation models face three key limitations, including coarse-grained user profiling, reliance on manually generated tags, and inadequate exploitation of structured information. To address these challenges, this study employs the TransD model for dynamic semantic modeling of heterogeneous entities and their complex relationships. Additionally, AIGC technology is employed to automatically extract latent interest dimensions, emotional characteristics, and semantic tags from user reviews, thereby constructing a high-dimensional user interest profile and a content tag completion system. Experiments are conducted using the MovieLens 100K, 1M, and 10M public datasets, with evaluation metrics including Mean Average Precision (MAP), user satisfaction scores, content coverage, click-through rate (CTR), and recommendation trust scores. The results demonstrate that the optimized model achieves hit rates of 0.878, 0.878, and 0.798, and MAP scores of 0.633, 0.637, and 0.574 across the three datasets. The user satisfaction scores are 0.89, 0.88, and 0.87, while the CTR values reach 0.35, 0.33, and 0.34, all of which significantly outperform traditional models. Notably, the proposed approach exhibits superior stability and semantic adaptability, particularly in cold start user scenarios and interest transition contexts. Therefore, this study provides a novel modeling approach that integrates structured and unstructured information for movie recommendation systems. Also, it contributes both theoretically and practically to the research fields of intelligent recommendation systems, knowledge graph embedding, and AIGC-based hybrid modeling.