Introduction

Method and a system for personalized multimedia recommendation. The system analyses the multimedia content and extracts the audio- visual features (attributes), grounded on the style of the movies, which form the Mise-en-sc`ene characteristics.  The system uses the extracted information to generate relevant recommendations for users.

Technical features

The process allows the multi-media recommender system to deal with the Cold Start problem, which occurs when the system is unable to accurately recommend a new multimedia item to the users, since the new item is added to the catalogue and no information (e.g., genre, cast, producer, date of production, ratings, reviews, tags, and description) is available for the movie item. This is a situation that typically occurs in social movie-sharing web applications where every day, hundred millions of hours of videos are uploaded by users with no related information. Actually, the system can be defined as a bridge between artistic view toward movie making and the technical view and fills the semantic gap between the artists and engineers. Development Stage: working algorithm tested on a wide dataset of movies.

Possible Applications

• Multimedia recommendation systems.

Advantages

• Totally automatic and it does not necessarily require human involvement;
• Can be adopted in the cold start new item situation, that is when the other techniques fail to work properly;
• Is not computationally expensive and is completely scalable to big data, in contrast to the other techniques which require expensive process such as data pre-processing.