GPU Accelerated Boosted Trees and Deep Neural Networks for Better Recommender Systems

Deotte, Chris; Liu, Bo; Schifferer, Benedikt; Titericz, Gilberto

doi:10.1145/3487572.3487605

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…Compared to a naive baseline, our model demonstrated a crossentropy gain of 30.5% for likes and 63.5% for retweets. For reference, the winning implementation 26 of 2021 ACM RecSys Challenge, outperformed the naive baseline by 23.6% for likes and 29.5% for retweets. However, a straightforward comparison is not meaningful in our case, as our implementation leverages crucial features that were not accessible during the challenge.…”

Section: Trainingmentioning

confidence: 99%

Skewed perspectives: examining the influence of engagement maximization on content diversity in social media feeds

Bouchaud

2024

J Comput Soc Sc

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This article investigates the information landscape shaped by curation algorithms that seek to maximize user engagement. Leveraging unique behavioral data, we trained machine learning models to predict user engagement with tweets. Our study reveals how the pursuit of engagement maximization skews content visibility, favoring posts similar to previously engaged content while downplaying alternative perspectives. The empirical grounding of our work contributes to the understanding of human-machine interactions and provides a basis for evidence-based policies aimed at promoting responsible social media platforms.

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Section: Trainingmentioning

confidence: 99%

Skewed perspectives: examining the influence of engagement maximization on content diversity in social media feeds

Bouchaud

2024

J Comput Soc Sc

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“…We adopted 3 popular and successful implementations of GBDT: LightGBM 7 , XGBoost 8 and CatBoost 9 . Thanks to their flexibility and robustness, they can easily adapt to different types of features, obtaining challenge-winning results [2,5].…”

Section: Dataset-level Ensemblementioning

confidence: 99%

Multi-stage Ensemble Model for Cross-market Recommendation

Bernardis¹

2022

Preprint

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This paper describes the solution of our team PolimiRank for the WSDM Cup 2022 on cross-market recommendation. The goal of the competition is to effectively exploit the information extracted from different markets to improve the ranking accuracy of recommendations on two target markets. Our model consists in a multi-stage approach based on the combination of data belonging to different markets. In the first stage, state-of-the-art recommenders are used to predict scores for user-item couples, which are ensembled in the following 2 stages, employing a simple linear combination and more powerful Gradient Boosting Decision Tree techniques. Our team ranked 4th in the final leaderboard.

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User-Agnostic Model for Prediction of Retweets Based on Social Neighborhood Information

Celayes

Domínguez

Barsotti

2023

Communications in Computer and Information Science

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GPU Accelerated Boosted Trees and Deep Neural Networks for Better Recommender Systems

Cited by 9 publications

References 5 publications

Skewed perspectives: examining the influence of engagement maximization on content diversity in social media feeds

Skewed perspectives: examining the influence of engagement maximization on content diversity in social media feeds

Multi-stage Ensemble Model for Cross-market Recommendation

User-Agnostic Model for Prediction of Retweets Based on Social Neighborhood Information

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