2019/06/27 by Himan Abdollahpouri, Abdollahpouri, Himan, Robin Burke +1
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Caching and Content Delivery #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.1906.11711
openalex publication_date 2019/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.