An Industrial Framework for Cold-Start Recommendation in Zero-Shot Scenarios

Huan, Zhaoxin; Zhang, Gongduo; Zhang, Xiaolu; Zhou, Jun; Wu, Qintong; Gu, Lihong; Gu, Jinjie; He, Yong; Zhu, Yanyan; Mo, Linjian

doi:10.1145/3477495.3536332

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…CF methods from providing appropriate recommendations due to the lack of historical interactions (Zhao et al 2022;Rajapakse and Leith 2022;Raziperchikolaei, Liang, and Chung 2021;Pulis and Bajada 2021;Du et al 2022a;Huan et al 2022;Zhu et al 2021;Sun et al 2021;Wang et al 2021;Chu et al 2023). To remedy this, existing methods align the feature representations with interactions (Meng et al 2020;Guo et al 2017), falling into two research lines.…”

Section: Related Workmentioning

confidence: 99%

Temporally and Distributionally Robust Optimization for Cold-Start Recommendation

Lin,

Wang,

Zhao

et al. 2024

AAAI

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Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address this issue, prior studies mainly introduce item features (e.g., thumbnails) for cold-start item recommendation. They learn a feature extractor on warm-start items to align feature representations with interactions, and then leverage the feature extractor to extract the feature representations of cold-start items for interaction prediction. Unfortunately, the features of cold-start items, especially the popular ones, tend to diverge from those of warm-start ones due to temporal feature shifts, preventing the feature extractor from accurately learning feature representations of cold-start items. To alleviate the impact of temporal feature shifts, we consider using Distributionally Robust Optimization (DRO) to enhance the generation ability of the feature extractor. Nonetheless, existing DRO methods face an inconsistency issue: the worse-case warm-start items emphasized during DRO training might not align well with the cold-start item distribution. To capture the temporal feature shifts and combat this inconsistency issue, we propose a novel temporal DRO with new optimization objectives, namely, 1) to integrate a worst-case factor to improve the worst-case performance, and 2) to devise a shifting factor to capture the shifting trend of item features and enhance the optimization of the potentially popular groups in cold-start items. Substantial experiments on three real-world datasets validate the superiority of our temporal DRO in enhancing the generalization ability of cold-start recommender models.

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Section: Related Workmentioning

confidence: 99%

Temporally and Distributionally Robust Optimization for Cold-Start Recommendation

Lin,

Wang,

Zhao

et al. 2024

AAAI

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“…is to estimate the conversion probability, which refers to the likelihood of a user using a coupon. As with other recommendation scenarios, researchers typically employ User-Item Click-Through-Rate (CTR) models (Sim and Lee 2014;Mutanen, Nousiainen, and Liang 2010;Huan et al 2022;Huangfu et al 2022) to handle this problem.…”

Section: Introductionmentioning

confidence: 99%

Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations

Fang,

Zhang,

Cui

et al. 2024

AAAI

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Accurate prediction of coupon usage is crucial for promoting user consumption through targeted coupon recommendations. However, in real-world coupon recommendations, the coupon allocation process is not solely determined by the model trained with the history interaction data but is also interfered with by marketing tactics desired to fulfill specific commercial goals.This interference creates an imbalance in the interactions, which causes the data to deviate from the user's natural preferences. We refer to this deviation as the matching bias. Such biased interaction data affects the efficacy of the model, and thus it is necessary to employ debiasing techniques to prevent any negative impact. We investigate the mitigation of matching bias in coupon recommendations from a causal-effect perspective. By treating the attributes of users and coupons associated with marketing tactics as confounders, we find the confounders open the backdoor path between user-coupon matching and the conversion, which introduces spurious correlation. To remove the bad effect, we propose a novel training paradigm named Backdoor Adjustment via Group Adaptation (BAGA) for debiased coupon recommendations, which performs intervened training and inference, i.e., separately modeling each user-coupon group pair. However, modeling all possible group pairs greatly increases the computational complexity and cost. To address the efficiency challenge, we further present a simple but effective dual-tower multi-task framework and leverage the Customized Gate Control (CGC) model architecture, which separately models each user and coupon group with a separate expert module. We instantiate BAGA on five representative models: FM, DNN, NCF, MASKNET, and DEEPFM, and conduct comprehensive offline and online experiments to demonstrate the efficacy of our proposed paradigm.

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“…Meta learning, especially optimization-based meta learning, has recently been widely applied to the cold-start problem in DLRM, effectively alleviating the problem [11,16,21,26,33]. The paradigm of optimization-based meta learning [15] usually consists of two update loops and datasets: the inner loop is to learn the taskspecific parameters using the support set and another outer loop is to update the meta parameters based on the formerly computed parameters using the query set.…”

Section: Introductionmentioning

confidence: 99%

G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems

Xiao,

Zhao,

Zhou

et al. 2023

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

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Recently, a new paradigm, meta learning, has been widely applied to Deep Learning Recommendation Models (DLRM) and significantly improves statistical performance, especially in cold-start scenarios. However, the existing systems are not tailored for meta learning based DLRM models and have critical problems regarding efficiency in distributed training in the GPU cluster. It is because the conventional deep learning pipeline is not optimized for two taskspecific datasets and two update loops in meta learning. This paper provides a high-performance framework for large-scale training for Optimization-based Meta DLRM models over the GPU cluster, namely G-Meta. Firstly, G-Meta utilizes both data parallelism and model parallelism with careful orchestration regarding computation and communication efficiency, to enable high-speed distributed training. Secondly, it proposes a Meta-IO pipeline for efficient data ingestion to alleviate the I/O bottleneck. Various experimental results show that G-Meta achieves notable training speed without loss of statistical performance. Since early 2022, G-Meta has been deployed in Alipay's core advertising and recommender system, shrinking the continuous delivery of models by four times. It also obtains 6.48% improvement in Conversion Rate (CVR) and 1.06% increase in CPM (Cost Per Mille) in Alipay's homepage display advertising, with the benefit of larger training samples and tasks.

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An Industrial Framework for Cold-Start Recommendation in Zero-Shot Scenarios

Cited by 7 publications

References 9 publications

Temporally and Distributionally Robust Optimization for Cold-Start Recommendation

Temporally and Distributionally Robust Optimization for Cold-Start Recommendation

Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations

G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems

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