REVIEW 4 cited by
The Unfairness of Popularity Bias in Recommendation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Recommender systems are known to suffer from the popularity bias problem: popular (i.e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations. Research in this area has been mainly focusing on finding ways to tackle this issue by increasing the number of recommended long-tail items or otherwise the overall catalog coverage. In this paper, however, we look at this problem from the users' perspective: we want to see how popularity bias causes the recommendations to deviate from what the user expects to get from the recommender system. We define three different groups of users according to their interest in popular items (Niche, Diverse and Blockbuster-focused) and show the impact of popularity bias on the users in each group. Our experimental results on a movie dataset show that in many recommendation algorithms the recommendations the users get are extremely concentrated on popular items even if a user is interested in long-tail and non-popular items showing an extreme bias disparity.
Forward citations
Cited by 4 Pith papers
-
Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations
Combining the context-aware LORE model with calibrated popularity re-ranking yields POI recommendations whose popularity distribution most closely matches users' historical preferences.
-
LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendation
LLM-derived Bayesian priors for comment recommendation improve click-through rate in the 10 to 49 impression cold-start range (gender prior +9.5%, content prior +7.8%), but not at the aggregate level.
-
NAM: A Normalization Attention Model for Personalized Product Search In Fliggy
A popularity-aware normalization and gating model (NAM) improves personalized product search on Fliggy by 0.8% in online conversion rate while adding 0.001 to offline CTCVR AUC.
-
Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
MTORL jointly learns channel recommendation and budget allocation for online advertising from offline user journeys, and reports better accuracy and reward than prior methods on KuaiRand, Criteo, and a Taobao A/B test.
Discussion (0). Sign in to comment.