binge

Inspiration

4 Indians in the team grew up watching movies from Bollywood, Hollywood etc. Drama is part of our life. In spite of the largest movie collection, we too search a lot for what movie to watch. And most of us can't remember NOT watching a movie a friend suggested.

What it does

Our general goal is to build a easy decision-making process of choosing movies for users, which is accomplished through the 2 key points: 1) Intelligent recommendation Analyze data through interest of the similarity with Facebook friends 2) Social elements Sharing watching experience, where original friends/new friends watch the same movie, chatting at the same time.

How we built it

We used APIs provided by themoviedb.org to get information about movies and links to their trailers. The data from the APIs were stored on a Google FireBase database and this DB was connected to the front end running on ReactJS.

Challenges we ran into

The lack of user data! We were very enthusiatic about building the recommendation based on the user history and suggest similar movies. That brought us some other interesting ML mechanism(Tinder like!) Time is the main issue, since we plan to use various methods to have precise recommendations, each method require a set of database with different sources and analysis model. Regarding user experience, we firstly stuck how to make the decision-making process fun and simple, until we successfully ideated the dating model.

Accomplishments that we're proud of

Beautiful intutive design Survived with 2 hours of sleep (Shows the passion for the challenge!)

What we learned

Start building early to complete your prototype! Do not give up even if its late

What's next for binge

Loads! Have a look at the design link attached! https://invis.io/X6ENE18QN You will love it! -Intelligent recommendation Tracking like/dislike history with supervised machine learning method. Prediction of emotional status. One way is to use affective computing model to analyze the current emotion with the data from other social platform; another way is through weather data, which really has some impact on people’s emotion in Finland.

Smooth interaction experience When firstly register, intuitive gamification way to know basic interest (movie genres) of new users. In “Discovery” tab which is intended for randomly searching and saving, using “Tinder” model for “movie dating”, where users can swipe the movie trails to express like/dislike on mobile phones.
Social elements By connecting to facebook account, people can know the preference and comments of their friends. Sharing watching experience, where original friends/new friends watch the same movie, chatting at the same time.

Implementation of effective computing methods and others Prototype testing and iterations