Fairness and Diversity in Recommender Systems: A Comprehensive Survey

Core Concepts

The authors explore the interconnectedness of fairness and diversity in recommender systems, highlighting the importance of considering both aspects for user satisfaction and system performance.

Abstract

The survey delves into the significance of fairness-aware and diversity-aware recommender systems. It discusses the connections between fairness and diversity, emphasizing the need to broaden perspectives for improved user experiences. The study categorizes user diversity into explicit/implicit features, historical preferences, fairness requirements, and multiple interests.
Recommender systems play a crucial role in providing personalized services across various domains. The primary goal is to improve utility performance, but solely focusing on this may lead to practical issues like bias and lack of diversity. Fairness-aware recommender systems have gained attention due to potential biases affecting stakeholders' interests.
Studies reveal that unfair recommendations can hinder small businesses' development by favoring popular items from big companies. User-level fairness ensures equitable treatment across different groups, while item-level fairness aims for equal opportunities among different item groups. Without considering diversity, recommender systems may recommend homogeneous items leading to user fatigue and decreased satisfaction.
Fairness works can be reinterpreted through a diversity perspective at both user and item levels. Strategies promoting fairness can alleviate disparate treatment based on different diversity metrics. By expanding the scope of diversity to incorporate user aspects as well, both user and item levels benefit from enhanced fairness.
Various surveys have focused on either fairness or diversity independently but fail to comprehensively discuss their connection. This survey fills this gap by exploring the intersection between these two domains while also covering them individually to provide context for readers.

Stats

Papers discussed in this survey along with public code links are available at: https://github.com/YuyingZhao/Awesome-Fairness-and-Diversity-Papers-in-Recommender-Systems.
Fairness Measurements include demographic parity-based exposure fairness, extract-K-based exposure fairness, disparity-based exposure fairness.
Individual Diversity metrics include Intra-List Diversity (ILD) measuring pairwise item diversity within one recommendation list.
Aggregate Diversity metrics include Aggregate Diversity (Count), Shannon Entropy, Gini Index.
Methods to promote Diversity include re-ranking-based methods, learning-to-rank methods, cluster-based methods, fusion-based methods.

Quotes

"Fairness works can be re-interpreted through a diversity perspective at both user and item levels."
"Strategies enhancing diversity have proven efficacious in improving fairness."
"Diversity is generally discussed from the item side."

Key Insights Distilled From

Fairness and Diversity in Recommender Systems

by Yuying Zhao,... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2307.04644.pdf

Deeper Inquiries

How can incorporating diverse perspectives enhance the overall effectiveness of recommender systems?

Incorporating diverse perspectives in recommender systems can significantly enhance their overall effectiveness. By considering a wide range of user preferences, interests, and characteristics, the system can provide more personalized recommendations that cater to individual needs. This leads to increased user satisfaction and engagement with the platform. Additionally, diversity in recommendations helps prevent users from being stuck in filter bubbles or echo chambers where they only receive similar content, thus promoting exposure to new ideas and products.
Diverse perspectives also contribute to mitigating biases within the system. By taking into account various demographic factors such as gender, race, age, etc., recommender systems can avoid reinforcing stereotypes or discrimination in their recommendations. This fosters a more inclusive and equitable experience for all users.
Moreover, incorporating diverse perspectives allows for better coverage of items across different categories or genres. This ensures that users are exposed to a wider variety of content and have access to niche or less popular items that align with their unique tastes.

What challenges might arise when balancing utility performance with considerations for fairness and diversity?

Balancing utility performance with considerations for fairness and diversity poses several challenges for recommender systems:

Trade-offs: Striking a balance between maximizing utility (e.g., accuracy of recommendations) while ensuring fairness (equal treatment across different user groups) and diversity (variety in recommended items) is complex as improvements in one aspect may lead to compromises in others.

Data Quality: Ensuring that data used by the system is unbiased and representative of all user groups is crucial but challenging due to potential biases present in historical interaction data.

Algorithmic Complexity: Implementing algorithms that optimize both utility metrics and fairness/diversity constraints simultaneously adds complexity to model training processes.

User Perception: Users may have varying perceptions of what constitutes fair or diverse recommendations based on personal preferences or beliefs which could impact their satisfaction with the system.

Evaluation Metrics: Defining appropriate evaluation metrics that capture both traditional utility measures along with fairness/diversity aspects accurately is essential but often requires careful consideration.

How can advancements in understanding user-level diversity contribute to more personalized recommendations?

Advancements in understanding user-level diversity play a vital role in enhancing personalized recommendations by:
1- Improved Recommendation Accuracy: Understanding explicit/implicit features about users enables tailoring suggestions based on individual characteristics leading to higher recommendation accuracy.
2- Enhanced Relevance: Historical preference analysis allows identifying patterns related to genre proportionality helping recommend relevant content aligned with past interactions.
3- Fairness Consideration: Recognizing sensitive attributes at an individual level aids avoiding biased suggestions ensuring fair treatment among diverse user groups.
4- Multiple Interest Incorporation: Considering multiple interests enhances recommendation breadth catering not just primary preferences but secondary ones too fostering richer experiences.
By leveraging insights into these facets of user-level diversity effectively through advanced algorithms like collaborative filtering models enriched with demographic-aware components or hybrid approaches blending content-based strategies tailored towards specific traits - recommender systems achieve deeper personalization offering tailored suggestions resonating closely with each unique individual's needs/preferences