Did you know that the global enterprise search market was valued at USD 4.21 billion in 2022?
Whenever someone performs a search, they expect to be greeted with results relevant to their requirements. However, traditional search techniques like “BM25 retrieval” return results based on how many times the phrase searched appears in a document.
While such techniques perform well to an extent, they fail to take into account the users’ unique preferences. This is where machine learning (ML) techniques come into play for helping deliver personalized results, particularly within the realm of federated search.
Maximizing Federated Search Relevance with ML Techniques
Unlike conventional methods, ML-driven federated search delves deeper into the intricacies of user interactions, considering many factors beyond keyword frequency.
This enables the system to discern patterns, user intent, and contextual relevance, therefore delivering a more personalized and tailored search experience. The following ML techniques help facilitate this:
Vector and Semantic Search
Vector search is a technique that leverages mathematical representations to understand and organize complex relationships between words and concepts, enhancing search accuracy. While semantic search focuses on interpreting the meaning of words and phrases within the context, providing a nuanced understanding of the users’ context.
These approaches excel in handling synonyms, misspellings, and variations in language, contributing to a higher level of search accuracy. They also boost personalization based on the users’ history and preferences, something which we could see lacking in the traditional approaches.
Query Understanding
Query Understanding involves analyzing user queries to grasp their intent, context, and semantics. It employs natural language processing (NLP) to interpret user input, discerning synonyms, and user-specific language, thus enhancing search engines’ ability to deliver more accurate and relevant results.
Using ML algorithms also enables the search engines to “Query rephrasing,” which essentially refers to suggesting alternate queries that would retrieve better results. Another technique, known as “Query expansion” helps expand the query to add related terms to the query and broadens its scope.
And voila! The search results powered by query understanding are much more thorough.
Neural Reranking
“Ranking” is often done based on how many times the search keyword appears in a document. However, neural reranking allows you to tune the search results so the top result is the most relevant one instead.
This is done by leveraging algorithms like k-nearest-neighbor (k-NN) for exact matches and approximate nearest-neighbor (ANN) for faster but slightly less accurate matches. Another benefit of neural reranking is that it can yield great results in zero-shot informational retrieval (IR) models ,i.e., models without much training.
Now if you were to implement these ML-based techniques for better search results, how would you determine if they’re performing as expected? By finding out their impact on recall and precision. Let’s dig deeper.
How do ML Techniques Enhance Recall & Precision in Search Results?
Precision refers to how accurate the search results are, while recall refers to the number of results returned. ML algorithms can ensure that the no relevant results are skipped over, and simultaneously rank the most relevant matches higher to reduce irrelevancy.
The following ML techniques help boost precision and recall:
- Word Embeddings: Embeddings are dense vector representations that capture semantic relationships and similarities between data points. They boost recall by capturing nuanced similarities in the data, allowing models to return better results.
- Cross-Encoders: These are neural networks that analyze pairs of things, such as questions and answers to determine how similar they are. The model gives a score to show how much the two things are connected or similar. This helps boost precision.
- User History: Taking the users’ history into account helps search engines narrow what they might be looking for. This historical data can be used to fine-tune the search results and provide more personalized and relevant answers.
Therefore, integrating ML techniques into your federated search solutions is a great way to amp up search relevancy. SearchUnify has been at it for quite some time! Keep reading to know more.
Delivering Search Excellence with SearchUnify’s ML Techniques
SearchUnify uses three types of ML-powered search techniques to boost recall as explained below.
Lexical Search
Lexical refers to the vocabulary or words used in a language, such as their structure and meaning. Lexical search involves searching for specific words or terms within a dataset or a corpus of text. It focuses on finding documents or information that contain the exact words or phrases specified in the search query.
Neural Search
Neural search leverages ML models known as neural networks to improve the efficiency and relevance of search results. It leverages advanced NLP techniques to understand the context, semantics, and relationships between words. This helps provide more accurate and contextually relevant search results by understanding the meaning behind the queries.
Hybrid Search
Hybrid search combines multiple search approaches or technologies to enhance the overall search experience. It often involves integrating traditional search methods with newer technologies like ML or artificial intelligence (AI).
For example, it might use lexical search for precise keyword matching and neural search for understanding context and providing more nuanced results. SearchUnify uses this combination to provide a more comprehensive and accurate search experience.
To improve precision, SearchUnify federated search leverages the following techniques.
Auto Boosting
This helps optimize search precision by adjusting the importance of different features, ensuring relevant information is prioritized based on user interactions and feedback.
Persona-based Results
ML algorithms can track and take into account the user personas and tailor the results to user-specific profiles, considering individual preferences and behavior to deliver more accurate and personalized information.
Cross Encoders
Cross encoders utilize neural networks to analyze relationships between different pieces of content, facilitating a deeper understanding of context and relevance for more accurate search results.
Named Entity Recognition (NER)
NER is an ML technique that involves the use of machine learning algorithms, particularly natural language processing models, to identify and classify entities (such as names of people, locations, organizations, etc.) within a given text. This helps refine and target search results with greater accuracy.
Step Into a New Era of Search
If you’re tired of irrelevant search results that are hampering your organization’s productivity, SearchUnify can help.
Feel free to sign up for our free demo and we’ll get right to helping you!
