Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance

As recommender systems become increasingly prevalent, the environmental impact and energy efficiency of training large-scale models have come under scrutiny. This paper investigates the potential for energy-efficient algorithm performance by optimizing dataset sizes through downsampling techniques in the context of Green Recommender Systems. We conducted experiments on the MovieLens 100K, 1M, 10M, and Amazon Toys and Games datasets, analyzing the performance of various recommender algorithms under different portions of dataset size. Our results indicate that while more training data generally leads to higher algorithm performance, certain algorithms, such as FunkSVD and BiasedMF, particularly with unbalanced and sparse datasets like Amazon Toys and Games, maintain high-quality recommendations with up to a 50% reduction in training data, achieving nDCG@10 scores within approximately 13% of full dataset performance. These findings suggest that strategic dataset reduction can decrease computational and environmental costs without substantially compromising recommendation quality. This study advances sustainable and green recommender systems by providing insights for reducing energy consumption while maintaining effectiveness.

e-Fold Cross-Validation for energy-aware Machine Learning Evaluations

This paper introduces e-fold cross-validation, an energy-efficient alternative to k-fold cross-validation. It dynamically adjusts the number of folds based on a stopping criterion. The criterion checks after each fold whether the standard deviation of the evaluated folds has consistently decreased or remained stable. Once met, the process stops early. We tested e-fold cross-validation on 15 datasets and 10 machine-learning algorithms. On average, it required 4 fewer folds than 10-fold cross-validation, reducing evaluation time, computational resources, and energy use by about 40%. Performance differences between e-fold and 10-fold cross-validation were less than 2% for larger datasets. More complex models showed even smaller discrepancies. In 96% of iterations, the results were within the confidence interval, confirming statistical significance. E-fold cross-validation offers a reliable and efficient alternative to k-fold, reducing computational costs while maintaining accuracy.

Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets

The recommender systems algorithm selection problem for ranking prediction on implicit feedback datasets is under-explored. Traditional approaches in recommender systems algorithm selection focus predominantly on rating prediction on explicit feedback datasets, leaving a research gap for ranking prediction on implicit feedback datasets. Algorithm selection is a critical challenge for nearly every practitioner in recommender systems. In this work, we take the first steps toward addressing this research gap. We evaluate the NDCG@10 of 24 recommender systems algorithms, each with two hyperparameter configurations, on 72 recommender systems datasets. We train four optimized machine-learning meta-models and one automated machine-learning meta-model with three different settings on the resulting meta-dataset. Our results show that the predictions of all tested meta-models exhibit a median Spearman correlation ranging from 0.857 to 0.918 with the ground truth. We show that the median Spearman correlation between meta-model predictions and the ground truth increases by an average of 0.124 when the meta-model is optimized to predict the ranking of algorithms instead of their performance. Furthermore, in terms of predicting the best algorithm for an unknown dataset, we demonstrate that the best optimized traditional meta-model, e.g., XGBoost, achieves a recall of 48.6%, outperforming the best tested automated machine learning meta-model, e.g., AutoGluon, which achieves a recall of 47.2%.

From Clicks to Carbon: The Environmental Toll of Recommender Systems

As global warming soars, evaluating the environmental impact of research is more critical now than ever before. However, we find that few to no recommender systems research papers document their impact on the environment. Consequently, in this paper, we conduct a comprehensive analysis of the environmental impact of recommender system research by reproducing a characteristic recommender systems experimental pipeline. We focus on estimating the carbon footprint of recommender systems research papers, highlighting the evolution of the environmental impact of recommender systems research experiments over time. We thoroughly evaluated all 79 full papers from the ACM RecSys conference in the years 2013 and 2023 to analyze representative experimental pipelines for papers utilizing traditional, so-called good old-fashioned AI algorithms and deep learning algorithms, respectively. We reproduced these representative experimental pipelines, measured electricity consumption using a hardware energy meter, and converted the measured energy consumption into CO2 equivalents to estimate the environmental impact. Our results show that a recommender systems research paper utilizing deep learning algorithms emits approximately 42 times more CO2 equivalents than a paper utilizing traditional algorithms. Furthermore, on average, such a paper produces 3,297 kilograms of CO2 equivalents, which is more than one person produces by flying from New York City to Melbourne or the amount one tree sequesters in 300 years.

Ensemble Boost: Greedy Selection for Superior Recommender Systems

Ensemble techniques have demonstrated remarkable success in improving predictive performance across various domains by aggregating predictions from multiple models [1]. In the realm of recommender systems, this research explores the application of ensemble technique to enhance recommendation quality. Specifically, we propose a novel approach to combine top-k recommendations from ten diverse recommendation models resulting in superior top-n recommendations using this novel ensemble technique. Our method leverages a Greedy Ensemble Selection(GES) strategy, effectively harnessing the collective intelligence of multiple models. We conduct experiments on five distinct datasets to evaluate the effectiveness of our approach. Evaluation across five folds using the NDCG metric reveals significant improvements in recommendation accuracy across all datasets compared to single best performing model. Furthermore, comprehensive comparisons against existing models underscore the efficacy of our ensemble approach in enhancing recommendation quality. Our ensemble approach yielded an average improvement of 21.67% across different NDCG@N metrics and the five datasets, compared to single best model. The popularity recommendation model serves as the baseline for comparison. This research contributes to the advancement of ensemble-based recommender systems, offering insights into the potential of combining diverse recommendation strategies to enhance user experience and satisfaction. By presenting a novel approach and demonstrating its superiority over existing methods, we aim to inspire further exploration and innovation in this domain.

The Potential of AutoML for Recommender Systems

Automated Machine Learning (AutoML) has greatly advanced applications of Machine Learning (ML) including model compression, machine translation, and computer vision. Recommender Systems (RecSys) can be seen as an application of ML. Yet, AutoML has found little attention in the RecSys community; nor has RecSys found notable attention in the AutoML community. Only few and relatively simple Automated Recommender Systems (AutoRecSys) libraries exist that adopt AutoML techniques. However, these libraries are based on student projects and do not offer the features and thorough development of AutoML libraries. We set out to determine how AutoML libraries perform in the scenario of an inexperienced user who wants to implement a recommender system. We compared the predictive performance of 60 AutoML, AutoRecSys, ML, and RecSys algorithms from 15 libraries, including a mean predictor baseline, on 14 explicit feedback RecSys datasets. To simulate the perspective of an inexperienced user, the algorithms were evaluated with default hyperparameters. We found that AutoML and AutoRecSys libraries performed best. AutoML libraries performed best for six of the 14 datasets (43%), but it was not always the same AutoML library performing best. The single-best library was the AutoRecSys library Auto-Surprise, which performed best on five datasets (36%). On three datasets (21%), AutoML libraries performed poorly, and RecSys libraries with default parameters performed best. Although, while obtaining 50% of all placements in the top five per dataset, RecSys algorithms fall behind AutoML on average. ML algorithms generally performed the worst.

Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems

The hyperparameters of recommender systems for top-n predictions are typically optimized to enhance the predictive performance of algorithms. Thereby, the optimization algorithm, e.g., grid search or random search, searches for the best hyperparameter configuration according to an optimization-target metric, like nDCG or Precision. In contrast, the optimized algorithm, internally optimizes a different loss function during training, like squared error or cross-entropy. To tackle this discrepancy, recent work focused on generating loss functions better suited for recommender systems. Yet, when evaluating an algorithm using a top-n metric during optimization, another discrepancy between the optimization-target metric and the training loss has so far been ignored. During optimization, the top-n items are selected for computing a top-n metric; ignoring that the top-n items are selected from the recommendations of a model trained with an entirely different loss function. Item recommendations suitable for optimization-target metrics could be outside the top-n recommended items; hiddenly impacting the optimization performance. Therefore, we were motivated to analyze whether the top-n items are optimal for optimization-target top-n metrics. In pursuit of an answer, we exhaustively evaluate the predictive performance of 250 selection strategies besides selecting the top-n. We extensively evaluate each selection strategy over twelve implicit feedback and eight explicit feedback data sets with eleven recommender systems algorithms. Our results show that there exist selection strategies other than top-n that increase predictive performance for various algorithms and recommendation domains. However, the performance of the top ~43% of selection strategies is not significantly different. We discuss the impact of our findings on optimization and re-ranking in recommender systems and feasible solutions.