Optimal Dataset Size for Recommender Systems: Evaluating Algorithms' Performance via Downsampling
This thesis investigates dataset downsampling as a strategy to optimize energy efficiency in recommender systems while maintaining competitive performance. With increasing dataset sizes posing computational and environmental challenges, this study explores the trade-offs between energy efficiency and recommendation quality in Green Recommender Systems, which aim to reduce environmental impact. By applying two downsampling approaches to seven datasets, 12 algorithms, and two levels of core pruning, the research demonstrates significant reductions in runtime and carbon emissions. For example, a 30% downsampling portion can reduce runtime by 52% compared to the full dataset, leading to a carbon emission reduction of up to 51.02 KgCO2e during the training of a single algorithm on a single dataset. The analysis reveals that algorithm performance under different downsampling portions depends on factors like dataset characteristics, algorithm complexity, and the specific downsampling configuration (scenario dependent). Some algorithms, which showed lower nDCG@10 scores compared to higher-performing ones, exhibited lower sensitivity to the amount of training data, offering greater potential for efficiency in lower downsampling portions. On average, these algorithms retained 81% of full-size performance using only 50% of the training set. In certain downsampling configurations, where more users were progressively included while keeping the test set size fixed, they even showed higher nDCG@10 scores than when using the full dataset. These findings highlight the feasibility of balancing sustainability and effectiveness, providing insights for designing energy-efficient recommender systems and promoting sustainable AI practices.
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
With the continuous increase of users and items, conventional recommender systems trained on static datasets can hardly adapt to changing environments. The high-throughput data requires the model to be updated in a timel…
Recommendation SystemsHeteFedRec: Federated Recommender Systems with Model Heterogeneity
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most existing FedRecs only allow participating …
Knowledge DistillationmodelRecommendation SystemsEvaluating Music Recommender Systems for Groups
Recommendation to groups of users is a challenging and currently only passingly studied task. Especially the evaluation aspect often appears ad-hoc and instead of truly evaluating on groups of users, synthesizes groups b…
BenchmarkingRecommendation SystemsOverview on NLP Techniques for Content-based Recommender Systems for Books
Recommender systems are an essential part of today{'}s largest websites. Without them, it would be hard for users to find the right products and content. One of the most popular methods for recommendations is content-bas…
Recommendation SystemsDo recommender systems function in the health domain: a system review
Recommender systems have fulfilled an important role in everyday life. Recommendations such as news by Google, videos by Netflix, goods by e-commerce providers, etc. have heavily changed everyones lifestyle. Health domai…
Collaborative FilteringDecision MakingRecommendation Systems