Using embeddings to detect duplicate business requests

Detecting duplicate business requests is a common challenge for many organizations, especially when dealing with a large volume of customer inquiries, support tickets, or orders. Traditional methods for identifying duplicates often rely on exact matches or simple keyword comparison, but these methods can miss requests that are phrased differently but essentially identical. This is where embeddings—vector representations of text—come into play.

What are Embeddings?

Embeddings are numerical representations of text where similar words or sentences are mapped to nearby points in a high-dimensional vector space. The idea is that semantically similar pieces of text will have similar embeddings. These embeddings are typically generated by models like Word2Vec, GloVe, or more advanced transformer-based models like BERT or OpenAI’s GPT.

By transforming text into embeddings, we can use mathematical techniques to determine how similar or different two pieces of text are. This is particularly useful when detecting duplicate business requests, as it enables a deeper understanding of meaning beyond simple word matches.

How Embeddings Help in Detecting Duplicate Business Requests

1. Understanding Semantics, Not Just Syntax:
   Embeddings capture the underlying meaning of text. This allows them to recognize requests that may use different words but have the same intent. For example, the requests “I need help resetting my password” and “Can you help me recover my password?” would be mapped to similar embeddings even though they use different phrases.

2. Handling Variations in Phrasing:
   Businesses often receive requests that are phrased in various ways, leading to challenges in duplicate detection. Embedding-based methods can handle these variations by clustering requests with similar meanings. Whether a customer asks about a “refund” or a “return,” the system can identify these as related requests.

3. Scalability:
   Embedding-based methods are highly scalable. When dealing with thousands or even millions of business requests, embeddings allow the system to efficiently compare and detect duplicates without manually reviewing each case. This makes them ideal for large-scale applications in customer service or e-commerce.

4. Improved Precision and Recall:
   Traditional methods may miss duplicates because they only look for exact word matches. Embeddings, on the other hand, measure the overall meaning of the text, resulting in better recall (finding more true duplicates) and higher precision (minimizing false positives).

Step-by-Step Approach to Using Embeddings for Duplicate Detection

1. Preprocessing Requests:
   The first step is to preprocess the business requests. This involves removing unnecessary characters, correcting typos, and normalizing the text (e.g., converting to lowercase). Some models may also require tokenization (splitting text into smaller units like words or subwords).

2. Generate Embeddings for Each Request:
   The next step is to generate embeddings for each business request. You can use pre-trained models like BERT, RoBERTa, or Sentence-BERT (which is optimized for sentence-level embeddings). These models convert each business request into a dense vector representation.

3. Calculate Similarity Between Requests:
   Once you have the embeddings, you can calculate the similarity between requests. Common similarity metrics for this task include cosine similarity, which measures the angle between two vectors. If the cosine similarity between two vectors is above a certain threshold (e.g., 0.85), they may be considered duplicates.

4. Clustering Similar Requests:
   Instead of comparing each request to every other request, which can be computationally expensive, clustering algorithms like DBSCAN or k-means can be used. These algorithms group similar requests together based on their embeddings. Any requests within the same cluster can be flagged as potential duplicates.

5. Thresholding and Duplicate Detection:
   After clustering, you can apply a threshold on the cosine similarity or use a combination of metrics to flag requests as duplicates. The threshold can be adjusted depending on the desired precision and recall.

6. Post-Processing and Refinement:
   Finally, the system may need some human oversight to fine-tune the duplicate detection. For example, requests with high semantic similarity but some differences in meaning (like urgent vs. non-urgent) might need to be reviewed to ensure they aren’t falsely flagged.

Example Use Cases of Embedding-based Duplicate Detection

1. Customer Support Tickets:
   When customers submit multiple tickets for the same issue, embedding-based systems can automatically flag these as duplicates, reducing redundancy and ensuring that the issue is resolved in a timely manner.

2. E-Commerce Order Requests:
   In e-commerce, customers might submit multiple requests regarding the same order. Embeddings can help detect when a customer has sent a similar inquiry more than once, helping support teams focus on genuinely unique issues.

3. Product or Service Feedback:
   Collecting feedback from customers across multiple channels (website, email, social media) can lead to duplicate responses. By using embeddings, businesses can aggregate similar feedback and gain clearer insights.

4. Internal Requests in Organizations:
   In large organizations, employees may submit repeated requests or queries for the same issue (e.g., IT requests for system access). Embedding-based detection can prevent redundancy and streamline the handling process.

Challenges and Considerations

1. Choosing the Right Model:
   The quality of the embeddings depends heavily on the model used. While models like BERT and RoBERTa are powerful, they may not always be the best fit for business-specific queries. Fine-tuning a pre-trained model on your own dataset might improve the results significantly.

2. Handling Ambiguities:
   Some business requests might be ambiguous, and embeddings may struggle to differentiate between them. For example, if two requests mention a “problem with the system,” the exact nature of the problem might not be clear from the request alone. Additional context may be needed.

3. Computational Resources:
   Embedding-based methods, especially when using large transformer models, can be computationally expensive. It is important to ensure that the system is optimized to handle the volume of requests in real time.

4. Continuous Improvement:
   Embedding models are not static; they need to be retrained and fine-tuned as the nature of business requests evolves over time. This requires continuous monitoring and updating of the system to keep it effective.

Conclusion

Using embeddings to detect duplicate business requests offers a powerful, scalable, and precise way to handle large volumes of customer interactions. By leveraging the semantic power of embeddings, businesses can more effectively identify similar or identical requests, reduce redundancy, and improve operational efficiency. However, it is essential to choose the right models, fine-tune them to your specific needs, and consider potential computational challenges for optimal performance. As machine learning continues to evolve, embedding-based approaches will play an increasingly central role in improving customer service and operational workflows.