Neural Search is a search approach that uses neural network models to improve the accuracy and relevance of results. These types of search engines are capable of understanding the meaning and relationship of words, reducing manual management of synonyms and language rules, as well as personalizing results according to the user's interests.
Traditional search engines display results based on the keywords entered by the user. That is, they show results that are most similar to what the visitor directly typed. For example, if a user types 'jeans', the search engine enters the database to find products that contain these exact words and then displays them as results.
This approach is relatively functional, and in most cases, it will help users find the product they are looking for. However, this search method is not infallible and can have many flaws.
What if the user makes a typo? Instead of typing 'jeans', they could type 'jeas', and there is a high probability that the search engine will not understand the entered keywords and bring up null or irrelevant results as it cannot find the term 'jeas' in the database.
Or, what if the user searches for symptoms rather than a product name? For example, instead of typing 'aspirin', they type 'headache pill'. An approach to search only by keywords would not bring up relevant results.
To deliver more accurate search results, in addition to offering better discovery experiences for users, new keyword search approaches are being developed. Let's review some of these new search methodologies.
Approaches to search
Keyword and search rules
Keyword search, used by most search engines, allows for delivering relevant and fast results to your customers. However, to increase the effectiveness of the search engine and minimize searches with no results, multiple search rules need to be created.
For example, a user may search for "billetera" instead of the more commonly used word "cartera." If this product is not registered with the name "billetera" in your database, the system will not display results. To address this problem, a rule can be created to consider "billetera" and "cartera" as synonyms in searches.
Search with Artificial Intelligence
Today, some search platforms have integrated Artificial Intelligence into their search engine to personalize search results according to the user.
A man and a woman may search for "running shoes" within an eCommerce platform.
However, the shoes each is looking for are completely different. The man may be thinking of men's size 9 black shoes, while the woman may be looking for women's size 5 light blue shoes.
By incorporating Artificial Intelligence, a search engine can analyze the user's past behavior (viewed products, purchased items, and search history) to display products according to their own interests.
This type of AI uses a variety of modern technologies, including machine learning and natural language processing, to deliver better search results.
Vector Search
Many new AI-based search engines incorporate vector search, which uses machine learning models to detect semantic relationships.
Search vectors are mathematical representations of text (both search terms and indexed information such as product information and web pages).
Vectors allow search engines to understand the context of the entered keywords. Basically, search keywords and the database are converted into vectors in order to understand the meaning of the search and provide results based on context.
Through vector search, a user can enter terms such as "clothing for hot climates", "clothing for heat wave" or "summer clothing", and receive relevant results for their search.
This is because the search engine understands that the user is looking for clothing to withstand hot weather.
In a keyword search, the user would be forced to write very specific words such as "linen shirt" or "cotton pants" to find clothing that fits their needs. Otherwise, they would get a failed search.
In contrast, vector-based search engines understand these types of word relationships, which allows them to overcome keyword-only searches.
Due to this additional dimension, vectors are the preferred option for tasks such as synonym automation, document grouping, meaning and intent detection in queries, and classification of search results.
Compared to keyword-based search engine results, vector search can be slower, not scale well, and be more expensive.
Now that we have understood the different approaches that search engines can take, we can move on to the latest and most advanced in search technology: Neural Search.
Advantages of Neural Search
Understands the meaning of words in context
Neural search engines are capable of understanding the meaning of words and detecting relationships between users, content, and data to provide personalized and accurate search results.
Neural search platforms can learn complex representations of data and utilize relationships between data points, which allows them to understand the real meaning behind a search query.
They can also learn arbitrarily complex representations from data and utilize relationships between data points, giving them an advantage when it comes to understanding the semantics of a search query.
For example, if a user searches for "battery," the neural search engine can infer whether the user is looking for information about electric batteries, car batteries, battery instruments, or kitchen sets, based on the context in which the word is used.
This leads to more precise and relevant search results for each of your users.
Allows for searching through texts, images, videos, audios, or 3D information
Neural Search can be used for any type of data, including images, videos, audio, and 3D information. A vector representing an image, for example, can contain information about its shape, color, and content, which is useful for relating it to other images.
Reduces manual search management
Neural Search is a pre-trained and optimized artificial intelligence. In this type of platform, it is not necessary to manually add most of the synonyms and language rules since the model already has them incorporated.
This means that search managers on websites do not have to constantly control and correct the search functionality by adding synonyms, establishing complex search rules, generating keyword-saturated titles, or linking additional language packs.
Automatically adjusts to offer more relevant and personalized search results
Neural Search is considered a form of Machine Learning. Machine Learning is a branch of Artificial Intelligence that allows computers to learn and improve through experience without being explicitly programmed. Neural Search uses Machine Learning to refine search results.
For example, if a user previously searched for "Samsung smartphone" and purchased a specific model, Neural Search can use this information to recommend related products such as headphones, chargers, or cases for that particular smartphone model.
In this way, Neural Search uses machine learning to refine and improve its search results, offering a more personalized and satisfying shopping experience for the user.
Delivers accurate results
Neural search engines use deep learning to enhance the accuracy and personalization of search results.
Deep learning helps search platforms better understand concepts and the relationships between them, and provide more accurate and relevant search results for users.
