Blockbuster: A New Perspective on Popularity-bias in Recommender Systems

Yalçin, Emre

doi:10.1109/ubmk52708.2021.9558877

Cited by 13 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a recent study [12], the authors have evaluated item popularity from a different perspective and hypothesized that the popularity of a product does not always mean that it is strongly preferable for users or vice versa. Therefore, they consider blockbuster items, both popular and highly-liked by users, and show that some well-known recommendation algorithms, including neighborhood-based CF ones, are unfortunately biased in favor of such blockbuster items in generated recommendations.…”

Section: Related Workmentioning

confidence: 99%

“…In RSs, the blockbuster term is used to describe items that are both popular (i.e., evaluated by many users) and highly liked (i.e., evaluated with high ratings) at the same time. The literature has verified that recommendation algorithms are biased toward such blockbuster items and therefore produce recommendation lists a few blockbuster items have dominated [12]. On the other hand, many other items in the catalog are under-represented in the produced recommendation lists.…”

Section: Definition Of Blockbuster Itemsmentioning

confidence: 99%

“…Recent studies have analyzed potential bias issues in favor of blockbuster items in generated recommendations [12] and tried to alleviate its adverse effects to achieve more qualified recommendations on beyond-accuracy sides such as coverage, diversity, and novelty [13]. In doing so, their primary concern is how blockbuster items should be defined and formulated.…”

Section: Definition Of Blockbuster Itemsmentioning

confidence: 99%

“…CF algorithms are generally evaluated based on their accuracy performances. However, recent research on RSs has realized that CF algorithms are strongly biased towards popular and highly-liked items, also called the blockbuster items [12], [13], in their produced referrals due to their internal mechanism or imbalances in the rating data. This issue leads to having ranked lists where such a few blockbuster items in the catalog have appeared too often, while other vast items can not get the deserved chance even when they might be desirable for users.…”

Section: Introductionmentioning

confidence: 99%

“…This issue leads to having ranked lists where such a few blockbuster items in the catalog have appeared too often, while other vast items can not get the deserved chance even when they might be desirable for users. Unfortunately, such blockbuster bias propagation of the CF algorithms leads to low-qualified recommendations for beyond-accuracy dimensions like coverage and diversity [12]. In addition, this bias leads to having a system where unfair competition occurs, as the products of different providers are not equally treated.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Effects of neighborhood-based collaborative filtering parameters on their blockbuster bias performances

Yalçin

2022

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

Collaborative filtering algorithms are efficient tools for providing recommendations with reasonable accuracy performances to individuals. However, the previous research has realized that these algorithms are undesirably biased towards blockbuster items. i.e., both popular and highly-liked items, in their recommendations, resulting in recommendation lists dominated by such blockbuster items. As one most prominent types of collaborative filtering approaches, neighborhood-based algorithms aim to produce recommendations based on neighborhoods constructed based on similarities between users or items. Therefore, the utilized similarity function and the size of the neighborhoods are critical parameters on their recommendation performances. This study considers three well-known similarity functions, i.e., Pearson, Cosine, and Mean Squared Difference, and varying neighborhood sizes and observes how they affect the algorithms’ blockbuster bias and accuracy performances. The extensive experiments conducted on two benchmark data collections conclude that as the size of neighborhoods decreases, these algorithms generally become more vulnerable to blockbuster bias while their accuracy increases. The experimental works also show that using the Cosine metric is superior to other similarity functions in producing recommendations where blockbuster bias is treated more; however, it leads to having unqualified recommendations in terms of predictive accuracy as they are usually conflicting goals.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Definition Of Blockbuster Itemsmentioning

confidence: 99%

Section: Definition Of Blockbuster Itemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effects of neighborhood-based collaborative filtering parameters on their blockbuster bias performances

Yalçin

2022

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

show abstract

Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches

Klimashevskaia

Elahi

Jannach

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesserknown items in a catalogue. Existing research, however, suggests that in many situations today's recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and we review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey therefore includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. We furthermore critically discuss today's literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.

show abstract

The Unfairness of Collaborative Filtering Algorithms’ Bias Towards Blockbuster Items

Yalçin

2023

Engineering Cyber-Physical Systems and Critical Infrastructures

View full text Add to dashboard Cite

Blockbuster: A New Perspective on Popularity-bias in Recommender Systems

Cited by 13 publications

References 26 publications

Effects of neighborhood-based collaborative filtering parameters on their blockbuster bias performances

Effects of neighborhood-based collaborative filtering parameters on their blockbuster bias performances

Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches

The Unfairness of Collaborative Filtering Algorithms’ Bias Towards Blockbuster Items

Contact Info

Product

Resources

About