Bars

Zhu, Jieming; Dai, Quanyu; Su, Liangcai; Ma, Rong; Liu, Jinyang; Cai, Guohao; Xiao, Xi; Zhang, Rui

doi:10.1145/3477495.3531723

Cited by 53 publications

(16 citation statements)

References 77 publications

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“…Since the datasets partition and evaluation metrics of all compared baselines are consistent with our method, we directly present the results reported in them, we can ensure that we present the optimal performance of various methods on the premise of fair comparison. The experimental setting of all baselines can be found at https://openbenchmark.github.io/BARS [38].…”

Section: Performance Comparisonmentioning

confidence: 99%

Personalized Graph Signal Processing for Collaborative Filtering

Liu,

Li,

et al. 2023

Preprint

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The collaborative filtering (CF) problem with only user-item interaction information can be solved by graph signal processing (GSP), which uses low-pass filters to smooth the observed interaction signals on the similarity graph to obtain the prediction signals.However, the interaction signal may not be sufficient to accurately characterize user interests and the low-pass filters may ignore the useful information contained in the high-frequency component of the observed signals, resulting in suboptimal accuracy. To this end, we propose a personalized graph signal processing (PGSP) method for collaborative filtering. Firstly, we design the personalized graph signal containing richer user information and construct an augmented similarity graph containing more graph topology information, to more effectively characterize user interests. Secondly, we devise a mixed-frequency graph filter to introduce useful information in the high-frequency components of the observed signals by combining an ideal low-pass filter that smooths signals globally and a linear low-pass filter that smooths signals locally. Finally, we combine the personalized graph signal, the augmented similarity graph and the mixed-frequency graph filter by proposing a pipeline consisting of three key steps: pre-processing, graph convolution and post-processing. Extensive experiments show that PGSP can achieve superior accuracy compared with state-of-the-art

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Section: Performance Comparisonmentioning

confidence: 99%

Personalized Graph Signal Processing for Collaborative Filtering

Liu,

Li,

et al. 2023

Preprint

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“…To begin, we train a full model with a fixed field dimension of 16, as suggested by previous works [48,74,75]. This process allows us to generate an informative embedding table for each field.…”

Section: Field-level Dimension Optimizationmentioning

confidence: 99%

“…Implementation details. We implement all models with Pytorch [40] and refer to existing works [8,74,75]. We use Adam [27] optimizer to optimize all models, and the default learning rate is 0.001.…”

Section: Experimental Analysis 51 Experiments Setupmentioning

confidence: 99%

“…For our proposed GCN, GDCN-S and GDCN-P, the default number of gated cross layer is 3 without specifically mentioned. For other baselines, we refer to two benchmark works (i.e., BARS [74] and FuxiCTR [75]) and their original literature to finetune their hyper-parameters.…”

Section: Experimental Analysis 51 Experiments Setupmentioning

confidence: 99%

“…Accurate CTR prediction can bring significant revenue gains and also improved user satisfaction [9,57,72]. Most methods typically consist of three layers [53,67,74]: feature embedding, feature interaction, and prediction. To improve the accuracy of CTR prediction, many methods have been proposed that focus on designing effective feature interaction architectures.…”

Section: Introductionmentioning

confidence: 99%

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Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction

Wang,

Gu,

et al. 2023

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

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Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising. It is crucial to effectively model feature interactions to improve the prediction performance of CTR models. However, existing methods face three significant challenges. First, while most methods can automatically capture high-order feature interactions, their performance tends to diminish as the order of feature interactions increases. Second, existing methods lack the ability to provide convincing interpretations of the prediction results, especially for high-order feature interactions, which limits the trustworthiness of their predictions. Third, many methods suffer from the presence of redundant parameters, particularly in the embedding layer. This paper proposes a novel method called Gated Deep Cross Network (GDCN) and a Field-level Dimension Optimization (FDO) approach to address these challenges. As the core structure of GDCN, Gated Cross Network (GCN) captures explicit high-order feature interactions and dynamically filters important interactions with an information gate in each order. Additionally, we use the FDO approach to learn condensed dimensions for each field based on their importance. Comprehensive experiments on five datasets demonstrate the effectiveness, superiority and interpretability of GDCN. Moreover, we verify the effectiveness of FDO in learning various dimensions and reducing model parameters. The code is available on https://github.com/anonctr/GDCN.

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