recommendation doc update

Author: prasankh

docs/source/user_guide/operators/recommender_operator/index.rst

@@ -38,10 +38,6 @@ This operator is ideal for a variety of applications, including:
 - **Streaming Services**: Suggesting movies, TV shows, or music based on user viewing or listening habits.
 - **Content Platforms**: Proposing articles, blogs, or news stories tailored to user interests.
 
-**Recommender Documentation**
-
-This documentation will delve into these concepts comprehensively, showcasing how to utilize the versatility and adaptability of the recommender operator within the Python library module to build recommendation systems.
-
 .. versionadded:: 2.11.14
 
 .. toctree::

docs/source/user_guide/operators/recommender_operator/quickstart.rst

@@ -21,8 +21,58 @@ Initialize your recommender job through the ads cli command:
    ads operator init -t recommender
 
 
-Input Data
------------
+Prepare Input Data
+-------------------
+
+The Recommender Operator requires three essential input files:
+
+1. **Users File**: Contains user information.
+2. **Items File**: Contains item information.
+3. **Interactions File**: Interactions between users and items.
+
+Sample Data
+===========
+
+**users.csv**:
+
+=========  ===  ======  ============  =========
+user_id    age  gender  occupation    zip_code
+=========  ===  ======  ============  =========
+1          24   M       technician    85711
+2          53   F       other         94043
+3          23   M       writer        32067
+4          24   M       technician    43537
+5          33   F       other         15213
+=========  ===  ======  ============  =========
+
+**items.csv**:
+
+===========  =================  ============  ======  =========  ==========  ========
+movie_id     movie_title        release_date  Action  Adventure  Animation   Children
+===========  =================  ============  ======  =========  ==========  ========
+1            Toy Story (1995)    01-Jan-1995   0       0          1          1
+2            GoldenEye (1995)    01-Jan-1995   1       1          0          0
+3            Four Rooms (1995)   01-Jan-1995   0       0          0          0
+4            Get Shorty (1995)   01-Jan-1995   1       0          0          0
+===========  =================  ============  ======  =========  ==========  ========
+
+**interactions.csv**:
+
+=======  =========  ======  ============
+user_id  movie_id   rating  timestamp
+=======  =========  ======  ============
+2        1          3       881250949
+4        2          3       891717742
+3        3          1       878887116
+1        4          2       880606923
+5        2          1       886397596
+2        3          4       884182806
+4        1          2       881171488
+=======  =========  ======  ============
+
+
+Configure the YAML File
+----------------------
 
 Within the ``recommender`` folder created above there will be a ``recommender.yaml`` file. This file should be updated to contain the details about your data and recommender.
 
@@ -49,8 +99,8 @@ Within the ``recommender`` folder created above there will be a ``recommender.ya
       interaction_column: rating
 
 
-Run
----
+Run the Recommender Operator
+----------------------------
 
 Now run the recommender job locally:
 
@@ -62,8 +112,25 @@ Now run the recommender job locally:
 Results
 -------
 
-If not specified in the YAML, all results will be placed in a new folder called ``results``. Performance is summarized in the ``report.html`` file.
+If not specified in the YAML, all results will be placed in a new folder called ``results``. Performance is summarized in the ``report.html`` file, and the recommendation results can be found in results/recommendations.csv.
 
 .. code-block:: bash
 
-    open results/report.html
+    vi results/recommendations.csv
+    open results/report.html
+
+Example Output (recommendations.csv):
+====================================
+
+=======  =========  ======
+user_id  movie_id   rating
+=======  =========  ======
+1        1          4.9424
+1        2          4.7960
+1        3          4.7314
+1        4          4.6951
+2        1          4.7893
+2        2          4.7870
+2        3          4.7624
+2        4          4.6802
+=======  =========  ======