CineVector HDNN: A Hybrid Deep Neural Network Architecture for Movie Recommendations

Description:

CineVector HDNN is an intelligent hybrid movie recommendation system that predicts and suggests movies similar to a selected title using a combination of semantic embeddings and metadata-based deep neural representations.

The system integrates:

·SBERT (Sentence-BERT) for extracting semantic meaning from movie overviews

·A Hybrid Deep Neural Network (HDNN) that learns 64-dimensional embeddings from movie metadata such as genres, cast, crew, keywords, popularity, ratings, runtime, and release year

By combining text understanding and metadata learning, CineVector HDNN provides accurate, meaningful, and human-like recommendations.

It is fast, scalable, and suitable for intelligent movie assistants and large recommendation platforms.

Input:

·Enter or select the name of a movie

Output:

·List of similar movies with:

• Title

• Release year

• Similarity score

• Genres

• Rating

Tags:

#CineVectorHDNN #MovieRecommendation #HybridDeepLearning #SBERT #NeuralNetworks #RecommenderSystems #AIinEntertainment #DeepLearning #MachineLearning #SemanticSearch

References:

1. Reimers, N., & Gurevych, I. (2019).Sentence-BERT: Sentence Embeddings using Siamese BERT Networks.— Provides the semantic embedding method used for movie overview understanding.

2. Koren, Y., Bell, R., & Volinsky, C. (2009).Matrix Factorization Techniques for Recommender Systems.— Foundation for embedding-based recommendation methods that inspired HDNN’s metadata representation.