Enterprise AI Analysis
BERT and CNN integrated Neural Collaborative Filtering for Recommender Systems
This experiment proposes a BERT and CNN-integrated Neural Collaborative Filtering (NCF) model to enhance recommender systems. The model leverages user and item profiles, handling numeric, categorical, and image data to extract latent features and predict user interest. Trained and validated on the MovieLens dataset, the proposed hybrid approach significantly outperforms baseline NCF and BERT-based NCF models, demonstrating improved recall and hit ratio by considering diverse data types.
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0.720
Enhanced Recommendation Recall
0.486
Improved Hit Ratio @ 10
25
Optimized Training Epochs
200%+
Improvement in Hit Ratio
Deep Analysis & Enterprise Applications
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Methodology
Key Findings
AI Architecture
Data & Training
The proposed Hybrid Neural Collaborative Filtering (HNCF) framework integrates BERT and CNN for robust recommendation. It's designed to process various data types—numerical (user/item IDs), categorical (movie features), and image (posters)—to extract rich contextual information and generalize user-item interactions effectively.
Enterprise Process Flow
Load Dataset
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Scraping images and related information
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Preprocess
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Train Test Split
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Pass through the data generator class and fed into the model
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Train the model
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Evaluate the model
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Recommend items to any given user
The experimental results demonstrate that the BERT-CNN-based HNCF model significantly outperforms traditional NCF and BERT-based NCF baselines. This highlights the substantial benefits of integrating diverse data types—especially categorical features via BERT and image data via CNN—into a unified recommendation framework.
| Model Name | Recall | Hit Ratio @ 10 (on 799 users) |
|---|---|---|
| Benchmark NCF [4] | 0.449 | 0.161 |
| BERT based NCF [3,7,8,9] | 0.689 | 0.422 |
| Proposed BERT-CNN based HNCF | 0.720 | 0.486 |
0.720
Peak Recall Score Achieved by HNCF
The proposed HNCF model is engineered with a hybrid architecture. It integrates Keras Embedding layers for IDs, a BERT encoder for contextual categorical features, and a VGG16 pre-trained CNN for image feature extraction. These components concatenate to form a comprehensive vector, processed by dense layers for final user-item interaction prediction.
Key Architectural Components for Enhanced Recommendations
The system leverages a sophisticated blend of deep learning models:
- Hybrid NCF Foundation: Integrates diverse neural networks atop a Neural Collaborative Filtering base for robust feature extraction and interaction learning.
- BERT Encoder: Utilizes a "BERT en uncased L-12 H-768 A-12" model to extract profound contextual meanings from categorical movie features like title, genres, and descriptions.
- VGG16 CNN (Pre-trained): Employs a pre-trained VGG16 model, with its top two layers discarded, to extract powerful visual features from movie posters.
- Keras Embedding Layers: Maps user and movie IDs into dense, fixed-size vector representations, capturing latent factors of user preferences and item characteristics.
- Multi-Layer Perceptron: A series of dense and dropout layers that process the concatenated features to learn complex non-linear interactions and output the final user-item interaction probability.
The experiment utilized a sampled and preprocessed subset of the MovieLens 20M dataset. The training involved 25 epochs with a batch size of 8, optimizing with Adam at a learning rate of 0.001. Performance was rigorously evaluated using Hit Ratio @ 10 and Recall, demonstrating the model's effectiveness.
Training Configuration Summary
- Dataset: MovieLens 20M Dataset (sampled to 1% then 0.02% of main dataset for feasibility, ~16k train records, ~4k validation records).
- Data Types Handled: Numerical (User ID, Movie ID), Categorical (movie features like title, genres, description), and Image (movie posters).
- Preprocessing: Categorical text transformed (lowercase, stop words, special characters removed) for BERT; images resized to (224,224,3), converted to NumPy array, and normalized.
- Training Epochs: 25 cycles over the entire training dataset.
- Batch Size: 8 samples processed per gradient update.
- Optimizer: Adam with a learning rate of 0.001 for efficient model weight adjustment.
- Metrics: Evaluated using Hit Ratio @ 10 and Recall to measure recommendation quality.
- Computational Environment: Kaggle notebook (72GB HDD, 13GB RAM, CPU, 14GB GPU).
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Your AI Implementation Roadmap
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Phase 1: Discovery & Strategy (2-4 Weeks)
Comprehensive assessment of your current systems, data infrastructure, and business objectives. We identify key opportunities for AI integration and define a tailored strategy that aligns with your enterprise goals.
Phase 2: Data Engineering & Model Prototyping (6-10 Weeks)
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