Overall schematic

README.md

@@ -1,12 +1,11 @@
-# user_preference_modeling
+# Multiple ways to model user preference in recommender systems
 
-Multiple ways to model user preference in recommender systems
+Modeling the preference of the user as an input to the retrieval or ranking model has been a successful strategy in recommender systems. In this repo, we will show how to do it effectively. We will use the example of a ranking model but the approach equally applies to a retrieval model. The overall schematic of a ranking model is in ![Fig 0: schematic_multi_task_estimator](./images/schematic_multi_task_estimator.png)
 
-Modeling the preference of the user as an input to the retrieval or ranking model has been a successful strategy in recommender systems.
-
-The conventional approach is using an embedding table lookup as shown in the image below and implemented in [multi_task_estimator.py](./src/multi_task_estimator.py).
+The conventional approach of representing a user is using an embedding table lookup as shown in the image below and implemented in [multi_task_estimator.py](./src/multi_task_estimator.py).
 ![Fig 1: user_id_embedding_lookup](./images/user_id_embedding_lookup.png)
 
+
 We will also look at the schematic of an implementation using Deep Hash Embeddings.
 ![Fig 2: deep_hash_embeddings](./images/deep_hash_embeddings.png)
 
@@ -24,4 +23,4 @@ If you want to allocate more of your memorization capacity to a certain cohort,
 
 ## Contributing
 
-Run `pytest tests/*` from main directory before submitting a PR.
+Run `pytest tests/*` from main directory before submitting a PR.