Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation
A core research question in recommender systems is to propose batches of highly relevant and diverse items, that is, items personalized to the user's preferences, but which also might get the user out of their comfort zone. This diversity might induce properties of serendipidity and novelty which might increase user engagement or revenue. However, many real-life problems arise in that case: e.g., avoiding to recommend distinct but too similar items to reduce the churn risk, and computational cost for large item libraries, up to millions of items. First, we consider the case when the user feedback model is perfectly observed and known in advance, and introduce an efficient algorithm called B-DivRec combining determinantal point processes and a fuzzy denuding procedure to adjust the degree of item diversity. This helps enforcing a quality-diversity trade-off throughout the user history. Second, we propose an approach to adaptively tailor the quality-diversity trade-off to the user, so that diversity in recommendations can be enhanced if it leads to positive feedback, and vice-versa. Finally, we illustrate the performance and versatility of B-DivRec in the two settings on synthetic and real-life data sets on movie recommendation and drug repurposing.
Code (0)
등록된 구현이 없습니다.
Tasks
Movie RecommendationPoint ProcessesSimilar Papers 제목 키워드 기반
Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algor…
DiversityOut-of-Distribution GeneralizationSynthetic Data GenerationBIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving
Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related pathways. We present BIRDS, a framework for Biodiversity Impact of Request…
Evolutionary Multi-Objective Diversity Optimization
Creating diverse sets of high quality solutions has become an important problem in recent years. Previous works on diverse solutions problems consider solutions' objective quality and diversity where one is regarded as t…
DiversitySelfAI: A self-directed framework for long-horizon scientific discovery
Scientific discovery increasingly entails long-horizon exploration of complex hypothesis spaces, yet most existing approaches emphasize final performance while offering limited insight into how scientific exploration unf…
Drug DiscoveryUnderstanding trade-offs in classifier bias with quality-diversity optimization: an application to talent management
Fairness,the impartial treatment towards individuals or groups regardless of their inherent or acquired characteristics [20], is a critical challenge for the successful implementation of Artificial Intelligence (AI) in m…
DiversityFairnessManagement