Social and Content Hybrid Image Recommender System for Mobile Social Networks by Faustino Sanchez, Marta Barrilero, Silvia Uribe, Federico Alvarez, Agustin Tena, Jose Manuel. Menendez.
Recommender System for Sport Videos Based on User Audiovisual Consumption by Sanchez, F. ; Alduan, M. ; Alvarez, F. ; Menendez, J.M. ; Baez, O.
A pair of papers I discovered at: New Model to Recommend Media Content According to Your Preferences, which summarizes the work as:
The traditional recommender system usually use: semantic techniques which result in products defined by themes, similar tags to the user interests, algorithms that use collective intelligence of a large set of user, in a way that this traditional system recommends themes that suit other people with similar preferences.
From this knowledge state, an applied model of multimedia content that goes beyond this paradigm has been developed, and it incorporates other features of whose influence, the user is not always aware and because of that reason has not been used so far in these types of systems.
Therefore, researchers at the UPM have analyzed in depth the audiovisual features that can be influential for users and they proved that some of these features that determine aesthetic trends and usually go unnoticed can be decisive when defining the user tastes.
For example, researchers proved that in a movie, the relative information to the narrative rhythm (shot length, scenes and sequences), the movements (camera or frame content) or the image nature (brightness, color, texture, information quantity) is relevant when cataloguing the preferences of each piece of information. Analogously to the movies, the researchers have analyzed images using a subset of descriptors considered in the case of video.
In order to verify this model, researchers used a database of 70,000 users and a million of reviews in a set of 200 movies whose features were previously extracted.
These descriptors, once they are standardized, processed and generated adequate statistical data, allow researchers to formally characterize the contents and to find the influence degree on each user as well as their preference conditions.
This makes me curious about how to exploit similar “unseen / unnoticed” factors that influence subject identification?
Both from a quality control perspective but also for the design of topic map authoring/consumption interfaces.
Our senses, as Scrooge points out: A slight disorder of the stomach makes them cheats.
Now we know they may be cheating and we are unaware of it.