[ruslandanilin]

In my opinion, you could represent "apple" as a vector, for example, [0.99, 0.3, 0.7] in relation to [fruits, computers, religion]. Then, you can create different user vectors that describe the interests of various groups. For instance, the general population might have a vector like [0.8, 0.2, 0.1], geeks as [0.6, 0.95, 0.05], and religious people as [0.7, 0.1, 0.95].

By creating these user vectors, you can compare them with the "apple" vector and find the best match using ANN. This approach allows you to determine which group is most interested in a given context or aspect of the word "apple." The ANN will help you identify similarities or patterns in the user vectors and the "apple" vector to find the most relevant matches.

Thank you

[Sai_]

I don’t know what ANN is but your comment raises two questions in my mind -

1. Where did your first vector of [0.99, 0.3, 0.7] come from? You later present the concept of user vectors which are vectors for different cohorts of users but don’t name the first vector as a user vector.

2. I feel my example of vectors for “general population users” and “bible literalist blogger” user aligns with your “user vector” concept.