The project was built as a part of the Microsoft Engage '22 Program. Contributors were required to Demonstrate through their app the different kinds of algorithms that a web-streaming app (like Netflix) or an audio-streaming app (like Spotify) may use for their Recommendation Engine within a span of 4 weeks. Content Based Recommender System recommends movies similar to the movie user likes.
Check out the live demo: https://movie-recommender-system-web.herokuapp.com/
- Top 10 recommended movie
- Overview, genre, title, rating, runtime, status of the movie
- Top cast of the movie
- Role the different cast played in the movie
- About top cast of the movie
- User Reviews
- About page to help the user know more about the website
- Authentication and login
- Updating Profile
- The site is completely responsive
The details of the movies(title, genre, runtime, rating, poster, etc) are fetched using an API by TMDB, https://www.themoviedb.org/documentation/api, and using the IMDB id of the movie in the API, I did web scraping to get the reviews given by the user in the IMDB site using beautifulsoup4 .
- Perseverance and debugging skills: Having faced numerous bugs that seemed impossible to solve at first to actually overcoming them felt like victory. It improved my problem solving skills and faith in hardwork.
- New Technologies: The project was a fun introduction to flask, content based recommendation and databases for me and I thoroughly enjoyed learning the new technologies. With the help of my friends and mentors as well as self study I was able to grow my knowledge.
- Growth mindset: The sessions as well as the project taught me that our skills can be improved and having a growth mindset helps one and their organization in the long run.
How does it decide which item is most similar to the item user likes? Here we use the similarity scores.
It is a numerical value ranges between zero to one which helps to determine how much two items are similar to each other on a scale of zero to one. This similarity score is obtained measuring the similarity between the text details of both of the items. So, similarity score is the measure of similarity between given text details of two items. This can be done by cosine-similarity.
Cosine similarity is a metric used to measure how similar the documents are irrespective of their size. Mathematically, it measures the cosine of the angle between two vectors projected in a multi-dimensional space. The cosine similarity is advantageous because even if the two similar documents are far apart by the Euclidean distance (due to the size of the document), chances are they may still be oriented closer together. The smaller the angle, higher the cosine similarity.
More about Cosine Similarity : Understanding the Math behind Cosine Similarity
- Clone or download this repository to your local machine.
- Install all the libraries mentioned in the [requirements.txt] file with the command
pip install -r requirements.txt - Open your terminal/command prompt from your project directory and run the file
run.pyby executing the commandpython run.py. - Go to your browser and type
http://127.0.0.1:5000/in the address bar. - Hurray! That's it.