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arXiv · 1906.11711

Reducing Popularity Bias in Recommendation Over Time

Himan Abdollahpouri·Robin Burke
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Abstract

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.

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BibTeXRIS

Himan Abdollahpouri, Robin Burke. 2019-06-27. Reducing Popularity Bias in Recommendation Over Time. https://arxiv.org/abs/1906.11711

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