As the data on various websites and applications increases, the computations happening behind the scenes start taking up time. You must have noticed how even ChatGPT takes a bit longer to answer your query compared to the amount of time it took when it was new. It all has to do with the large amounts of data that we are working with. When dealing with huge chunks of data and information it becomes important to implement techniques that handle this data appropriately which makes it easier to process, store, and retrieve it. Talking about optimized techniques for processing, storing, and retrieving data from huge databases, vector search comes into the picture. In this article, let’s explore what is vector search and how it is being used in Natural Language Processing.
What is Vector Search?
To understand what is this whole talk of vector search about, we first need to have a look at what exactly is a vector. Vector is a mathematical term meaning a representation of data in a multi-dimensional space. These vectors are used to represent various types of data, such as text, images, or any other structured or unstructured information. Vector Search is an algorithm that searches for information in a database by mapping each data item to a vector representation of itself. The key innovation behind vector search lies in these vectors capturing not just the raw data but also the relationships and similarities between data items.
What is Natural Language Processing?
Since we are going to talk about the applications of vector search in Natural Language Processing, firstly let’s see what exactly is Natural Language Processing. Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. The primary goal of NLP is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. Natural Language Processing (NLP)sometimes involves leveraging knowledge gained from pre-training on one task and applying it to improve performance on a different but related task.
Applying Vector Search in Natural Language Processing
The way vector search has been helpful in Natural Language Processing is by enabling us to store textual data in the form of vectors. “Well what help does doing that provide?”, you must be wondering. With that, NLP systems can efficiently navigate vast information spaces, leading to improvements in information retrieval, question answering, and document similarity.
Enhanced Information Retrieval
As the name suggests, vector search is something that makes it easier to search for certain information in a large database. The process of information retrieval enhances significantly whe vector search is used with Natural Language Processing. This enhancement comes in handy for platforms like search engines and query responses.
Advanced Document Similarity
By transforming documents into vector representations, the system can measure the similarity between documents with a higher level of granularity. This facilitates more accurate identification of content overlap, leading to improved plagiarism detection. Moreover, vector search enhances content recommendation systems by understanding the underlying semantic relationships between documents, thus offering more relevant and personalized suggestions to users.
Applications of Vector Search
A search algorithm as advanced as Vector Search has numerous applications for businesses and organizations. Let’s have a look at some of the fields and aspects in which Vector Search is proving to be a helping hand.
- Netflix: Netflix uses vector search to recommend movies and TV shows based on a user’s viewing history. It considers the content of what you’ve watched and suggests similar titles.
- Amazon: Amazon employs vector search to recommend products to users. If you search for a particular product, it suggests related items that others have found interesting or purchased together.
- Google Images: Google Images allows users to search for images using keywords. It also uses vector search to find visually similar images. For example, if you search for “Eiffel Tower,” it can show you pictures of the Eiffel Tower from various angles and sources.
- Virtual Assistants: Virtual assistants like Siri and Google Assistant utilize vector search to understand and respond to spoken or typed queries, providing answers that match the user’s intent.
- Spotify: Spotify employs vector search to suggest music tracks and playlists based on your listening history and preferences. It can recommend songs with similar musical characteristics to your favorite tracks.
- Ad Targeting: Advertisers use vector search to target ads to users based on their interests and online behavior, increasing the relevance of advertisements.
Limitations of Vector Search
Now, of course, Vector Search algorithms too, just like any other algorithm have some limitations to it.
- High-Dimensional Space: Since the dimensional space used to map vectors is multi-dimensional, the data points become sparse which can impact the efficiency and accuracy of similarity calculations.
- Data Quality: The quality of data wholly depends on the quality of the vector representations. If a correct Vector Space Model is not chosen to represent data points as vectors, the quality of data retrieval will have to suffer.
- Lack of Historical Data: Recommender systems using vector search may struggle when dealing with new users or items because there is insufficient historical data to create meaningful vectors.
Vector Search v/s Traditional Search
We understand that you will not be willing to agree that Vector Search algorithms are better than Traditional Search algorithms without looking at facts and figures. So here’s a detailed analysis of the same just for you:
| Aspect | Vector Search | Traditional Search |
| Query Approach | Semantic understanding of context and meaning | Keyword-based with exact matching |
| Matching Technique | Similarity matching between vectors | String matching based on keywords |
| Context Awareness | High, understands context and intent | Limited, relies on specific keywords |
| Handling Ambiguity | Handles polysemy and word ambiguity | Vulnerable to keyword ambiguity |
| Data Types | Versatile, works with various data types | Primarily text-based search |
| Efficiency | Efficient, suitable for large datasets | May become less effective as data scales |
| Examples | Content recommendation, image search | Standard web search, database queries |