Abstract

zMovie adds a whole new dimension to the movie watching experience by providing real-time personalized movie recommendations to users. It takes a collaborative social-networking approach where a userís own tastes are mixed with that of the entire community to generate meaningful results.

Most existing movie services like IMDB do not personalize their recommendations but simply provide an overall rating for a movie. This significantly decreases the value of each recommendation as it does not cater to the individual movie preferences of the user. Unlike these systems, zMovieís Recommendation Engine will continually analyze individual userís movie preferences and recommend custom movie recommendations. The overall goal is to ease the movie discovery process.

zMovie is purely a movie recommendation service in that it offers a list of movie suggestions based on previous user ratings. zMovie is designed not to search for movies but to discover them through our recommendation process. zMovie will allow users to rate movies they have seen. This data is then analyzed, and recommendations are then returned to the user. The core of our project, zMovieís recommendation algorithm, is based on a cluster-smoothed collaborative filtering algorithm [2]. We have refined and tuned the parameters around this algorithm by comparing our predicted ratings against actual ratings using in-sample and out-of-sample techniques as well as analyzing live user feedback.