680 research outputs found
InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models
Deep learning-based recommender models (DLRMs) have become an essential
component of many modern recommender systems. Several companies are now
building large compute clusters reserved only for DLRM training, driving new
interest in cost- and time- saving optimizations. The systems challenges faced
in this setting are unique; while typical deep learning training jobs are
dominated by model execution, the most important factor in DLRM training
performance is often online data ingestion.
In this paper, we explore the unique characteristics of this data ingestion
problem and provide insights into DLRM training pipeline bottlenecks and
challenges. We study real-world DLRM data processing pipelines taken from our
compute cluster at Netflix to observe the performance impacts of online
ingestion and to identify shortfalls in existing pipeline optimizers. We find
that current tooling either yields sub-optimal performance, frequent crashes,
or else requires impractical cluster re-organization to adopt. Our studies lead
us to design and build a new solution for data pipeline optimization, InTune.
InTune employs a reinforcement learning (RL) agent to learn how to distribute
the CPU resources of a trainer machine across a DLRM data pipeline to more
effectively parallelize data loading and improve throughput. Our experiments
show that InTune can build an optimized data pipeline configuration within only
a few minutes, and can easily be integrated into existing training workflows.
By exploiting the responsiveness and adaptability of RL, InTune achieves higher
online data ingestion rates than existing optimizers, thus reducing idle times
in model execution and increasing efficiency. We apply InTune to our real-world
cluster, and find that it increases data ingestion throughput by as much as
2.29X versus state-of-the-art data pipeline optimizers while also improving
both CPU & GPU utilization.Comment: Accepted at RecSys 2023. 11 pages, 2 pages of references. 8 figures
with 2 table
Exploring Deep Space: Learning Personalized Ranking in a Semantic Space
Recommender systems leverage both content and user interactions to generate
recommendations that fit users' preferences. The recent surge of interest in
deep learning presents new opportunities for exploiting these two sources of
information. To recommend items we propose to first learn a user-independent
high-dimensional semantic space in which items are positioned according to
their substitutability, and then learn a user-specific transformation function
to transform this space into a ranking according to the user's past
preferences. An advantage of the proposed architecture is that it can be used
to effectively recommend items using either content that describes the items or
user-item ratings. We show that this approach significantly outperforms
state-of-the-art recommender systems on the MovieLens 1M dataset.Comment: 6 pages, RecSys 2016 RSDL worksho
A novel evaluation framework for recommender systems in big data environments
Henriques, R., & Pinto, L. (2023). A novel evaluation framework for recommender systems in big data environments. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2023.120659---We gratefully acknowledge the support of Aptoide in providing access to the data which made this project possible. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.Recommender systems were first introduced to solve information overload problems in enterprises. Over the last few decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media, and mobile app stores. Several methods have been proposed over the years to build recommender systems. However, very little work has been done in recommender system evaluation metrics. The most common approach to measuring recommender system’s performance in offline settings is to employ micro or macro averaged versions of standard machine-learning measures. Profit or other business-oriented metrics have been proposed for other predictive analytics problems, such as churn prediction. However, no such metrics have emerged for the recommender system context. In this work, we propose a novel evaluation metric that incorporates information from the online-platform userbase’s behavior. This metric’s rationale is that the recommender system ought to improve customers’ repeatead use of an online platform beyond the baseline level (i.e. in the absence of a recommender system). An empirical application of this novel metric is also presented in a real-world mobile app store, which integrates the dynamics of large-scale big data environments, which are common deployment scenarios for these types of recommender systems. The resulting profit metric is shown to correlate with the existing metrics while also being capable of integrating cost information, thereby providing an additional business benefit context, which allows us to differentiate between two similarly performing models.publishersversionepub_ahead_of_prin
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