Embedding-Based Retrieval: Enhancing Search Efficiency
Understanding the Concept
Embedding-based retrieval aims to create a shared semantic space where both queries and items are represented as dense vectors. This allows for matching based on meaning rather than just keywords, making searches more effective. Related items are positioned closer together, facilitating faster retrieval using Approximate Nearest Neighbour (ANN) methods, which are essential for handling large datasets.
Challenges in Traditional Retrieval Systems
Most retrieval systems aim to return a set number of items for each query, but this approach has its limits. For popular queries, a broader range of results may be needed to capture all relevant items, while focused queries may return too many irrelevant results, impacting accuracy. This happens partly because traditional methods often treat all queries the same way, ignoring their unique characteristics.
Introducing Probabilistic Embedding-Based Retrieval (pEBR)
To address these challenges, researchers have developed pEBR, which uses a probabilistic method to adapt the retrieval process based on the specific needs of each query. By utilizing a dynamic cosine similarity threshold based on the distribution of relevant items, pEBR can effectively balance the results for various types of queries. This approach improves both recall (the completeness of results) and precision (the relevance of results).
Key Contributions of the Research
- The introduction of a two-tower model that represents items and queries in a shared semantic space.
- Identification of popular loss functions in retrieval systems and their significance.
- Proposals for new loss functions based on contrastive and maximum likelihood estimation to enhance performance.
- Experimental validation showing significant improvements in retrieval accuracy.
- Ablation studies revealing how different model components contribute to overall performance.
Engage with Us
For more insights and updates, check out the original paper and follow us on Twitter, join our Telegram Channel, and connect on LinkedIn. If you appreciate our work, you’ll love our newsletter. Join our thriving ML SubReddit community of over 55k members.
Transform Your Business with AI
Stay competitive and leverage pEBR to enhance your search capabilities. Here’s how you can integrate AI into your operations:
- Identify Automation Opportunities: Find key areas in customer interactions that can benefit from AI.
- Define KPIs: Ensure your AI initiatives have measurable impacts on your business.
- Select AI Solutions: Choose customizable tools that fit your specific needs.
- Implement Gradually: Start with pilot projects, gather data, and expand wisely.
Connect with Us
For advice on AI KPI management, reach out at hello@itinai.com. Stay updated on AI insights through our Telegram channel t.me/itinainews or follow us on Twitter @itinaicom.
Explore how AI can revolutionize your sales processes and customer engagement at itinai.com.
