Now, have you ever found yourself relying more and more on recommender systems, and sometimes the results you search for yourself don't seem to be as accurate as the AI recommendations?
So I guess some people are wondering, how is the recommender system so "smart"? The answer is: "vector search". Today, let's talk about this technology and see how it has changed the way we get information.
1. What the hell is a vector search?
First of all, what exactly is vector search? Simply put, it's a search technique that "gets you".
Traditional search engines typically go for content that is an exact match based on the keywords you type in. But vector search is different, it's smarter and not only matches keywords, but tries to understand your true intent and context and then goes for the content that best meets your needs.
Think of it as an "old friend" who knows what you want, even if you don't seem to have made it clear.
The 2 obvious applications of vector search arerecommender systemcap (a poem)repositoryVector search is behind everything, from shopping and music recommendations to knowledge base retrieval. Whether it's shopping, music recommendations, or knowledge base searches, vector search is behind the scenes.
For example, if you listen to a song on a certain music platform, the platform will not only recommend songs in a similar style, but will also recommend you some even more similar songs based on the emotions expressed in the song and the content of the lyrics.
2, the core of the vector search is vector and dimension
So how does vector search do all this? The key lies in "vector" and "dimension".
In math, vectors have direction and size, and in vector search, text or data is transformed into a "high-dimensional vector".
Each dimension represents a different characteristic of the data, such as sentiment, semantics, or context. Imagine that these vectors become points in a high-dimensional space, and the process of searching is to find the closest point to your needs in this complex space.
The above explanation may be a bit abstract, but it can be understood in the following way: traditional search is like on a flat mapPrecise point finding, whereas vector search is performed in a 3D stereoscopic space (multidimensional space(used in a newspaper or magazine)Find an approximation., and the considerations are more numerous and complex.
3, vector search changes the search
Vector search has not only made search smarter, it has also changed the way we access information.
Under the information society, information is growing explosively, and the data is not only large in volume but also very confusing.
Vector search transforms this data into a form that we can understand and manipulate. It not only helps us to find the exact information, but also to find the closest information through multiple dimensions, including synonyms, meaning, intent, and context.
Vector search not only searches for individual words, but also analyzes complex relationships between words, and thus a better understanding of whether each choice is closer to or deviates from the meaning of the retrieved sentence.
In this way, we not only acquire information, but find more meaningful results.
In the past, we needed to enter very precise keywords to find the information we wanted, but now vector searches can help us find the most relevant content even if the description is vague.
This is so convenient for the average user, who doesn't need to figure out all the specialized terminology, but can simply use natural language to roughly articulate their needs and get accurate results.
4. Summary
The emergence of vector search has quietly changed the way we access information, marking a leap in search technology. It is making its mark in areas such as recommender systems and generative AI.
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