In this project, I have developed a search engine optimization (SEO) system that mimics the core functionality of Google Search. The system leverages Recurrent Neural Networks (RNNs) for semantic understanding of queries and documents, and a two-tower architecture for efficient ranking and retrieval.
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RNN-based Semantic Understanding: The system uses RNNs to capture the contextual meaning and relationships within queries and documents, enabling more accurate semantic matching.
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Two-Tower Architecture: The model is designed with a two-tower architecture, where one tower encodes the query, and the other tower encodes the documents. This allows for efficient ranking and retrieval by comparing the encoded representations.
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Streamlit Deployment: The SEO system is deployed as a user-friendly web application using the Streamlit framework, allowing for easy interaction and demonstrations.
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Data Preprocessing: Handling the complexities of real-world search data, including cleaning, tokenization, and feature engineering.
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Embedding Optimization: Experimenting with both off-the-shelf and fine-tuned embeddings to achieve the best performance.
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Hyperparameter Tuning: Carefully tuning the model hyperparameters, such as learning rate, batch size, and network architecture, to ensure optimal performance.
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Scalability: Addressing the scalability challenges of the system to handle large-scale search data and queries.
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Evaluation Metrics: Selecting and implementing appropriate evaluation metrics to assess the system's effectiveness in ranking and retrieving relevant documents.
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Incorporating User Feedback: Implementing mechanisms to incorporate user feedback and preferences to further improve the ranking and retrieval results.
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Multimodal Integration: Exploring the integration of other data modalities, such as images or videos, to enhance the search experience.
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Personalization: Developing personalized search models that adapt to individual user preferences and search histories.
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Efficiency Optimization: Investigating techniques to improve the system's efficiency, such as indexing or approximate nearest neighbor search.
I would like to acknowledge the research and development efforts of the Google Search team, whose work has inspired and informed the development of this project. Additionally, I'm grateful for the open-source tools and libraries that have made this project possible, including PyTorch, Streamlit, and the various NLP and information retrieval resources available.