# Personalisation with vector search

**Assumed Knowledge**: Vectors, Vector Search\
**Target Audience**: Data scientist, Vector enthusiasts, Business analysts, Executives\
**Reading Time:** 3 minutes

Vectors provide a simple way of personalising search and tailoring these searches for the individual. As is proposed in the original [Word2Vec](https://arxiv.org/pdf/1301.3781.pdf) paper (Mikolov, T., Chen, K., Corrado G., Dean J.), vectors  have relationships. The famous example given is:

![King - Man + Woman = Queen](https://imgr.whimsical.com/object/Kz1KTqzLpNDj5yuQTiC2rT)

When we consider the following example,  when we subtract the man vector from the king vector and add it to the woman vector, the closest vector is the queen vector (when we exclude the other vectors used to form the equation).

$$
King - Man + Woman = Queen
$$

This is very useful as this means that vectors in the vector space hold relationships This famous example can be extended into how we can use vectors for **personalisation.** 

Let us extend this to a **retail use case study** to better understand how personalisation works with vectors. 

**Simplified Retail Use Case**

Let us consider a blender. An individual may have been shopping for a lot of blenders and have clicked on a lot of Samsung labeled blenders. 

If we decide to take the average of their vectors, we will associate this used with the Samsung Blender vector. 

The individual may then decide to search for kettles. Based on our previous searches for Samsung blenders, we may then decide to personalise his search towards Samsung kettles. This is done using a similar equation as above: 

![Samsung Blender - Blender + Kettle = Samsung Kettle](https://imgr.whimsical.com/object/MMkUhg8QeDh3Sndee6GYoM)

From this, we subtract the Samsung blender from the blender category and add it to the kettle category to get the Samsung Kettle. The Samsung kettle is therefore a personalised search result based on the user's preference of Samsung blenders.


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