Reducing Popularity Bias in Recommendation Over Time

06/27/2019
by   Himan Abdollahpouri, et al.
0

Many recommendation algorithms suffer from popularity bias: a small number of popular items being recommended too frequently, while other items get insufficient exposure. Research in this area so far has concentrated on a one-shot representation of this bias, and on algorithms to improve the diversity of individual recommendation lists. In this work, we take a time-sensitive view of popularity bias, in which the algorithm assesses its long-tail coverage at regular intervals, and compensates in the present moment for omissions in the past. In particular, we present a temporal version of the well-known xQuAD diversification algorithm adapted for long-tail recommendation. Experimental results on two public datasets show that our method is more effective in terms of the long-tail coverage and accuracy tradeoff compared to some other existing approaches.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/31/2019

The Unfairness of Popularity Bias in Recommendation

Recommender systems are known to suffer from the popularity bias problem...
research
01/22/2019

Managing Popularity Bias in Recommender Systems with Personalized Re-ranking

Many recommender systems suffer from popularity bias: popular items are ...
research
02/27/2022

The Unfairness of Popularity Bias in Book Recommendation

Recent studies have shown that recommendation systems commonly suffer fr...
research
12/05/2021

Long-Tail Session-based Recommendation from Calibration

Accurate prediction in session-based recommendation has achieved progres...
research
07/07/2023

A Network Resource Allocation Recommendation Method with An Improved Similarity Measure

Recommender systems have been acknowledged as efficacious tools for mana...
research
06/05/2020

Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation

Increasing aggregate diversity (or catalog coverage) is an important sys...
research
08/24/2023

On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis

Multimodal-aware recommender systems (MRSs) exploit multimodal content (...

Please sign up or login with your details

Forgot password? Click here to reset