Implicit data collection

Recommender systems
Concepts
Collective intelligence Relevance Star ratings Long tail
Methods and challenges
Cold start Collaborative filtering Dimensionality reduction Implicit data collection Item-item collaborative filtering Matrix factorization Preference elicitation Similarity search
Implementations
Collaborative search engine Content discovery platform Decision support system Music Genome Project Product finder
Research
GroupLens Research MovieLens Netflix Prize ACM Conference on Recommender Systems
v t e

Implicit data collection is used in human–computer interaction to gather data about the user in an implicit, non-invasive way.

Overview

The collection of user-related data in human–computer interaction is used to adapt the computer interface to the end user. The data collected are used to build a user model. The user model is then used to help the application to filter the information for the end user. Such systems are useful in recommender applications, military applications (implicit stress detection) and others.

Channels for collecting data

The system can record the user's explicit interaction and thus build an MPEG7 usage history log. Furthermore, the system can use other channels to gather information about the user's emotional state. The following implicit channels have been used so far to get the affective state of the end user:

facial activity
posture activity
hand tension and activity
gestural activity
vocal expression
language and choice of words
electrodermal activity
eye tracking

Emotional spaces

The detected emotional value is usually described any of the two most popular notations:

a 3D emotional vector: valence, arousal, dominance
degree of affiliation to the 6 basic emotions (sadness, happiness, anger, fear, disgust, surprise)^{[citation needed]}