Memory effect of the online user preference

TL;DR

This paper investigates the memory effects in online user preferences, revealing power-law distributed memory durations and proposing a Markovian model that captures varying memory strengths in user behavior.

Contribution

It uncovers the power-law distribution of user memory durations and introduces a simple Markovian model to simulate different memory effects in online preferences.

Findings

Memory durations follow a power-law distribution.

The proposed model can replicate various memory effects.

Long-duration memory is more common than random chance.

Abstract

The mechanism of the online user preference evolution is of great significance for understanding the online user behaviors and improving the quality of online services. Since users are allowed to rate on objects in many online systems, ratings can well reflect the users' preference. With two benchmark datasets from online systems, we uncover the memory effect in users' selecting behavior which is the sequence of qualities of selected objects and the rating behavior which is the sequence of ratings delivered by each user. Furthermore, the memory duration is presented to describe the length of a memory, which exhibits the power-law distribution, i.e., the probability of the occurring of long-duration memory is much higher than that of the random case which follows the exponential distribution. We present a preference model in which a Markovian process is utilized to describe the users' selecting behavior, and the rating behavior depends on the selecting behavior. With only one parameter for each of the user's selecting and rating behavior, the preference model could regenerate any duration distribution ranging from the power-law form (strong memory) to the exponential form (weak memory).