Supervised Learning - TV-Show Recommender

Table of Contents

1. How to Run the Program
2. Project Overview
3. Dataset
4. Model and Algorithm
5. Features
6. Requirements
7. Libraries
8. Classes
9. References

How to Run the Program

Prerequisites

1. Download and Extract the Dataset:

   • Download the dataset from TMDB TV Dataset.
   • Extract TMDB_tv_dataset_v3.zip into the dataset/ folder, so it contains the file TMDB_tv_dataset_v3.csv.

2. Install Dependencies:

   • Install the necessary libraries listed in requirements.txt (see below).

Running the Program

There are two ways to run the program, depending on whether you prefer to use the web-based interface or the command-line interface (CLI).

Web Interface (Flask)

To run the web-based interface (Flask application):

python app.py

• This will start a local web server, and you can access the app through your browser (usually at http://127.0.0.1:5000/).
• The program will load the dataset, prompt you to enter a TV show title, and ask how many recommendations you want.

Command-Line Interface (Python-GUI)

To run the command-line version of the program:

python main.py

• The program will work in the terminal, asking you to enter the title of a TV show you like and how many recommendations you want.

Note

The first time the program is run, it will generate Sentence-BERT embeddings. This can take up to 5 minutes due to the large size of the dataset.

---

Project Overview

The TV-Show Recommender is a machine learning-based program that suggests TV shows to users based on their preferences. The system uses Nearest Neighbors (NN) and K-Nearest Neighbors (K-NN) algorithms with cosine distance to recommend TV shows. Users provide a title of a TV show they like, and the system returns personalized recommendations based on similarity to other TV shows in the dataset.

---

Dataset

The dataset used in this project is sourced from TMDB (The Movie Database). It contains over 150,000 TV shows and includes information such as:

• Title of TV shows
• Genres
• First/Last air date
• Vote count and average rating
• Director/Creator information
• Overview/Description
• Networks
• Spoken languages
• Number of seasons/episodes

Download the dataset from here.

---

Model and Algorithm

The recommender system is based on Supervised Learning using the NearestNeighbors and K-NearestNeighbors algorithms. Here's a breakdown of the process:

1. Data Preprocessing:

   • The TV show descriptions are vectorized using Sentence-BERT embeddings to create dense vector representations of each show's description.

2. Model Training:

   • The NearestNeighbors (NN) algorithm is used with cosine distance to compute similarity between TV shows. The algorithm finds the most similar shows to a user-provided title.

3. Recommendation Generation:

   • The model generates a list of recommended TV shows by finding the nearest neighbors of the input title using cosine similarity.

---

Features

1. Data Loading & Preprocessing:

   • Loads the TV show data from a CSV file and preprocesses it for model training.

2. Model Training with K-NN:

   • Trains a K-NN model using the NearestNeighbors algorithm for generating recommendations.

3. User Input for Recommendations:

   • Accepts user input for the TV show title and the number of recommendations.

4. TV Show Recommendations:

   • Returns a list of recommended TV shows based on similarity to the input TV show.

---

Requirements

Data Requirements:

The dataset should contain the following columns for each TV show:

• Title
• Genres
• First/Last air date
• Vote count/average
• Director
• Overview
• Networks
• Spoken languages
• Number of seasons/episodes

User Input Requirements:

• TV Show Title: The name of the TV show you like.
• Number of Recommendations: The number of recommendations you want to receive (default is 10).

---

Libraries

The following libraries are required to run the program:

• pandas: For data manipulation and analysis.
• scikit-learn: For machine learning algorithms and preprocessing.
• scipy: For scientific computing (e.g., sparse matrices).
• time: For working with time-related functions.
• os: For interacting with the operating system.
• re: For regular expression support.
• textwrap: For text wrapping and formatting.
• flask: For creating the web interface.

To install the dependencies, run:

pip install -r requirements.txt