Conversational Movie Recommendation System

Movie Recommendation in Conversational

Steps To Run projects

Step 1: Create Slack Bot user

Step 2: Create a IBM Watson account and Upload the bot.json workspace

 skill-sam.json file in nlp 

Step 3: Install required packages

pip install pandas
pip install numpy
pip install sklearn
pip install nltk
python -m nltk.downloader stopwords
python -m nltk.downloader punkt
pip install slackclient
pip install ibm-watson

Step 4: Update the config files with the Slack and Watson API details

Please make sure that you modified the API details both for Slack and Watson in the config.py file

Step 5: Download data from source and perform Data Preparation

The data for this example is downloaded from the location below,

https://www.kaggle.com/rounakbanik/movie-recommender-systems/data

Name of the dataset - movies_metadata.csv

Step 6: Create "onetime.txt" file

Navigate to the folder where the main.py file resides and execute the code below.

python nlp/nlp_solutions/onetime_run_file.py

This will create the "onetime.txt" file automatically. If you need to rename this file, update the name in "config.py" file.

Step 7: Initiate Bot

Navigate to the folder where the main python script exists and run the code below.

python main.py

Working of the Bot

Step 1 (User asks question):

Users can interact with Sam via Slack. Once the user post a question via the interface, the question is passed to the backend system for analysis

Step 2 (NLP processing):

All the natural language processing happens in step 2.

Step 3 (Return the NLP results):

After the NLP processing is completed, we have three outputs from it

1. Intents - What the user is trying to ask or query?
2. Entities - What is the exact field or column they are looking for?
3. Dialog/Interaction - Provide the appropriate request/response for the user question.

Step 4 and 5(Query the data):

Currently, the data resides in a excel file. However, you can add multiple databases/excel files if needed, to access different sources. Based on the results from step 3, the appropriate database/excel file is queried and the results are returned.

Step 6 (Post the result to user):

The results obtained from the backend is posted to user via Slack

Step 7 (Log maintenance):

The interactions between the users are logged and stored in a flatfile format in a log file. Also, if the bot is not able to identify the user questions it will add those questions to a followup file.