*** originally published on boxesandarrows.com ***

This article investigates “content recommender” systems(recommendation systems) . Because Netflix is probably the best known recommendation system and numerous articles have been published about their system, I will concentrate on their content recommendation mechanism as representative of the type.

I will show that the Netflix mechanism contains characteristics of updated theories of emotion—mainly constructed emotions theory—but it still lacks several essential components.

The lack of these components can explain some inaccuracies in Netflix recommendations and can suggest broader implications.

Emotions: A background

The traditional view of emotions (Paul Ekman [1], as an example) is that people are born with a set of emotions—fear, anger, sadness, and the like.

Because we are all born with emotions, the traditional view is that these basic emotions are similar across all human beings.

Lisa Barrett’s recent research [2] has uncovered difficulties with this traditional theory.

One of the leading new theories is the constructed emotions theory. According to this view, emotions are learned, not born. Different people, therefore, have different emotions; cultural environment influences these emotions.

No emotion is universal, meaning some cultures have anger, sadness, fear, disgust, happiness and so on, and some cultures don’t.

The process of emotion begins in the brain. The brain tries to identify the physical environment, to understand what this environment has signified in the past, and what the cultural norms are related to this scenario. Following this analysis, the brain suggests an emotion most suitable to this context.

The context itself is also a factor; a different environment with identical traits would produce a different interpretation and thus a different feeling.

A simplified history of recommendation systems

A content recommendation area—such as Netflix’—shares general characteristics with the emotion analysis field.

With recommendations, we try to understand what the next thing a person would want to do or feel—such as when a person wants to feel frightened.

The prevailing opinion has been that as information accumulated by the recommendation system increases, accuracy will increase: More data, more accuracy. However, in the video recommendation field, the recommendations remain inaccurate despite the enormous amount of data available.

In a recommendation system, the system must analyze both the user and the content. My claim is that the main effort is focused on understanding the user rather than the content.

If we use the wrong method to understand what people want, no amount of data will make it more accurate.

Therein lies the problem.

User analysis

Theory of recommendations

The first generation of recommendation systems created a theory of recommendations; for example, if Thando watched sports content, Thando was probably a man and would want to see other testosterone-dominated content.