Imagine sitting on your couch, in front of your TV and wondering which movie to watch? If you know what you want to watch, you could just search for it. However, if you just wanted to watch a movie, but not a particular one, Search does not help. Further, most recommendation engines are informed by your watch and browse history, so they offer you more of similar movies.

Welcome to the Movie Recommendation Engine — it provides a Mood widget to help discover movies to match your mood. As you play with the sliders on the widget, it recommends movies based on partitioning the movie space based on emotions as prescriptive vectors. The result is a better experience and shorter path to selection.

Over the Top services that stream movies present recommendations to their users based on their browsing, viewing, and search history. One of the challenges is that user’s mood can vary from day to day based on extraneous factors such as time of the day, day of the week, the company they have and other factors. Consequently, Discovering movies and other premium content is challenging and time-consuming for most users.

Jaman Recommendation Engine is a SaaS platform that delivers smart movie recommendations to users (via any App that uses its APIs) leveraging its patented multi-criteria search technology. It enables the user to get suggestions through a Movie finder widget based on user’s mood. The Mood widget provides a set of emotions, or prescriptive vectors, to simplify the user experience of discovering the movie. Moods like Serious — Funny; Mellow — Charged; Deep — Shallow; Tears — Bullets are presented as sliders for the user to get interactive recommendations.

Jaman recommendation Engine (JRE) delivers smart movie recommendations using a faceted search service with a “slider widget” or “Mood Finder” widget that recommends movies based on user’s mood — Fun, Bullets, Tears, Serious; using cosine similarity metrics. Apart from collaborative filtering, the recommendations are informed by users’ interest graph, derived through processing their browsing and viewing history, their reviews and ratings, Taste profile, and other social media activities.