toplogo
Sign In

Enhancing Diversity in Personalized Recommender Systems through User-Centric Pre-processing


Core Concepts
A user-centric pre-processing approach to improve the diversity of top-N recommendations while maintaining recommendation performance.
Abstract
The paper introduces a novel pre-processing approach to enhance the diversity of personalized recommender systems. The key highlights are: The proposed approach employs a user-centric pre-processing strategy that selectively adds and removes a percentage of interactions from user profiles. This personalization ensures the recommender system remains closely aligned with user preferences while gradually introducing distribution shifts. The pre-processing technique offers flexibility and can seamlessly integrate into any recommender architecture. Extensive experiments are conducted on two public datasets for news and book recommendations, testing various standard and neural network-based recommender algorithms. The results show that the pre-processed data leads to recommender systems achieving comparable or improved performance compared to those trained on original data. Regarding diversity, the approach consistently improves normative diversity metrics like calibration, while descriptive diversity measures like coverage show mixed results. Additionally, the pre-processed data results in higher fair-nDCG scores, indicating enhanced exposure fairness and better representation of minority categories. The authors conclude that their user-centric pre-processing approach can effectively diversify recommendations without compromising accuracy, promoting both user satisfaction and provider fairness.
Stats
The MIND news dataset contains 17 news categories, with the training set having 9,368 user-item interactions and the test set having 15,557 interactions. The GoodBook dataset contains 31 book genres, with the training set having 8,477 user-item interactions and the test set having 4,715 interactions.
Quotes
"Our approach employs a user-centric pre-processing strategy aimed at exposing users to a wide array of content categories and topics." "Our pre-processing technique offers flexibility and can seamlessly integrate into any recommender architecture." "Our results show that our approach generates diverse recommendations, ensuring users are exposed to a wider range of items."

Deeper Inquiries

How can the proposed pre-processing approach be extended to handle dynamic user profiles and evolving item catalogs

The proposed pre-processing approach can be extended to handle dynamic user profiles and evolving item catalogs by incorporating real-time data updates and adaptive algorithms. Real-time Data Updates: Implement a mechanism to continuously update user profiles based on recent interactions and preferences. This can involve monitoring user behavior in real-time and adjusting the user profiles accordingly. For example, if a user starts showing interest in a new category, the system should dynamically update their profile to reflect this change. Adaptive Algorithms: Utilize machine learning techniques such as online learning or reinforcement learning to adapt to changes in user preferences and item catalogs. These algorithms can learn from new data and adjust the recommendations in real-time. By continuously updating the model based on the latest information, the system can provide more personalized and relevant recommendations. Incremental Updates: Instead of retraining the entire model from scratch, implement incremental updates to incorporate new data. This approach allows the system to efficiently adapt to changes without the need for extensive computational resources. Dynamic Feature Engineering: Develop dynamic feature engineering techniques that can capture temporal patterns in user behavior and item characteristics. By considering the temporal dynamics of user interactions and item popularity, the system can better adapt to evolving preferences and trends.

What are the potential drawbacks or unintended consequences of introducing distribution shifts in user profiles, and how can they be mitigated

Introducing distribution shifts in user profiles can have potential drawbacks and unintended consequences that need to be carefully addressed to ensure the effectiveness and fairness of the recommender system. Overfitting: Introducing distribution shifts may lead to overfitting, where the model becomes too specialized to the modified data and loses generalization capabilities. This can result in recommendations that are too tailored to the modified profiles and may not reflect the true preferences of users. Bias Amplification: Distribution shifts could amplify existing biases in the data, leading to skewed recommendations that reinforce stereotypes or discrimination. It is essential to monitor and mitigate bias in the pre-processing steps to ensure fair and diverse recommendations. User Trust: Sudden changes in user profiles may impact user trust in the recommender system. Users may be skeptical of recommendations that seem drastically different from their usual preferences. Providing transparency and explanations for the changes can help alleviate concerns and build trust. Evaluation Metrics: Distribution shifts may affect the evaluation metrics used to assess the performance of the recommender system. It is crucial to consider how these shifts impact metrics such as accuracy, diversity, and fairness and adjust the evaluation framework accordingly. To mitigate these drawbacks, it is important to conduct thorough testing and validation of the pre-processing approach, monitor the impact on key performance indicators, and incorporate feedback mechanisms to continuously improve the system.

How can the insights from this work on diversity be applied to other recommendation domains beyond news and books, such as music, e-commerce, or social media

The insights from this work on diversity in recommender systems can be applied to various recommendation domains beyond news and books, such as music, e-commerce, or social media, by considering the following strategies: Category Diversification: Implement category-based diversification techniques to ensure users are exposed to a wide range of items across different genres, artists, products, or content types. By promoting diversity in recommendations, the system can cater to a broader range of user preferences. Fairness and Inclusion: Incorporate fairness and inclusion principles in the recommendation process to ensure equitable representation of different user groups and item categories. By promoting diversity and fairness, the system can mitigate biases and provide a more inclusive experience for all users. Personalization: Tailor the diversification strategies to individual user preferences and behavior patterns. By personalizing the recommendations based on user profiles, the system can enhance user satisfaction and engagement. Dynamic Adaptation: Implement dynamic adaptation mechanisms to handle evolving user preferences and item catalogs. By continuously updating user profiles and adjusting recommendations in real-time, the system can stay relevant and responsive to changing user needs. By applying these insights to diverse recommendation domains, organizations can enhance the user experience, improve recommendation quality, and promote diversity and fairness in their systems.
0