Cobot: Modeling long term user activity with time

What are users getting interested in with time?

How are the interests changing/evolving?

These are two important questions Cobot tries to ask and answer. More specifically, Cobot uses domain specific dictionaries (for Health and Education domains) to extract concepts from user's conversations and deciphers user's short term and long term interests based on her conversations.

Here, you will see a couple of LTM (Long term model) graphs for a user asking questions on a site about Math topics in March this year snapshot-ed every 3 days (or more depending on activity).

In these graphs, you will see some new terms getting added, and associations between terms (based on multiple co-occurrences in STM) developing and decaying with time.

What We are trying to do is to heuristically infer some parameters like window for snapshot based on activity(related to user's short term and long term memories), learning and unlearning rates (how fast are users learning and unlearning things - related to semantic memory) for every user, etc. This modeling (done well) eventually helps Cobot to pick right users for recommending in different conversations. (We do a Spreading Activation search in user's LTM graphs mixed with other techniques for user recommendation.)

(There is a similar vector space based model for modeling user's STM (short term model) interests as well in Cobot.)