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Reducing Popularity Bias in Recommendation Over Time by Abdollahpouri, Himan; Burke, Robin is a scholarly article available to read on EtoBox.

What is Reducing Popularity Bias in Recommendation Over Time about?

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.

Author

Abdollahpouri, Himan; Burke, Robin

Published

2019

Language

EN