Warm Up Cold-start Advertisements

Pan, Feiyang; Li, Shuokai; Ao, Xiang; Tang, Pingzhong; He, Qing

doi:10.1145/3331184.3331268

Cited by 110 publications

(20 citation statements)

References 34 publications

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“…To validate the recommendation performance of the proposed method in different phases, we preprocess the datasets following [51,26,55]. Specifically, we label items as old or new based on their frequency, where items with more than N labeled instances are old and others are new.…”

Section: Experiments Settingmentioning

confidence: 99%

“…-DropoutNet [39] improves the robustness by applying dropout technology to mitigate the model's dependency on item ID. -Meta embedding (Meta-E) [26] learns the ID embedding for new items fast adaption with meta-learning technology.…”

Section: Backbones and Baselinesmentioning

confidence: 99%

“…The existing methods proposed to solve the item cold start problem mainly fall into three categories: 1) promoting the robustness of the recommendation model in absence of item ID embedding, such as using dropout or masking on item ID embedding in the model training [39,53,32]; 2) improving the learning efficiency with a limited amount of interaction data, such as using meta-learning approaches to quickly adapt the recommendation system to new item [37,26,35,11]; and 3) leveraging the side information of items to facilitate the initialization of the item ID embedding [47,55,57,4].…”

Section: Introductionmentioning

confidence: 99%

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Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up

Zhang¹,

Zheng²,

Ren³

et al. 2023

Preprint

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With numerous pieces of information emerging daily and greatly influencing people's lives, large-scale recommendation systems are necessary for timely bridging the users with their desired information. However, the existing widely used embedding-based recommendation systems have a shortcoming in recommending new items because little interaction data is available for training new item ID embedding, which is recognized as item cold start problem. The gap between the randomly initialized item ID embedding and the well-trained warm item ID embedding makes the cold items hard to suit the recommendation system, which is trained on the data of historical warm items. To alleviate the performance decline of new items recommendation, the distribution of the new item ID embedding should be close to that of the historical warm items. To achieve this goal, we propose an Adversarial Variational Autoencoder Warm-up model (AVAEW) to generate warm-up item ID embedding for cold items. Specifically, we develop a conditional variational autoencoder model to leverage the side information of items for generating the warm-up item ID embedding. Particularly, we introduce an adversarial module to enforce the alignment between warm-up item ID embedding distribution and historical item ID embedding distribution. We demonstrate the effectiveness and compatibility of the proposed method by extensive offline experiments on public datasets and online A/B tests on a real-world large-scale news recommendation platform.

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Section: Experiments Settingmentioning

confidence: 99%

Section: Backbones and Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up

Zhang¹,

Zheng²,

Ren³

et al. 2023

Preprint

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“…One promising research line falls into the category of content-based methods, which usually train a generative model to project a cold item's content, e.g. attributes, text, and image, etc., onto a warm item embedding space [2,3,16,19,21,24,27,32,46]. For example, DropoutNet [32] implicitly transforms a cold-start item's content to a warm embedding with randomly dropping the warm item embeddings that are learned during the training process.…”

Section: Introductionmentioning

confidence: 99%

Contrastive Collaborative Filtering for Cold-Start Item Recommendation

Zhou,

Zhang,

Yang

2023

Preprint

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The cold-start problem is a long-standing challenge in recommender systems. As a promising solution, content-based generative models usually project a cold-start item's content onto a warm-start item embedding to capture collaborative signals from item content so that collaborative filtering can be applied. However, since the training of the cold-start recommendation models is conducted on warm datasets, the existent methods face the issue that the collaborative embeddings of items will be blurred, which significantly degenerates the performance of cold-start item recommendation. To address this issue, we propose a novel model called Contrastive Collaborative Filtering for Cold-start item Recommendation (CCFCRec), which capitalizes on the co-occurrence collaborative signals in warm training data to alleviate the issue of blurry collaborative embeddings for cold-start item recommendation. In particular, we devise a contrastive collaborative filtering (CF) framework, consisting of a content CF module and a co-occurrence CF module to generate the content-based collaborative embedding and the co-occurrence collaborative embedding for a training item, respectively. During the joint training of the two CF modules, we apply a contrastive learning between the two collaborative embeddings, by which the knowledge about the co-occurrence signals can be indirectly transferred to the content CF module, so that the blurry collaborative embeddings can be rectified implicitly by the memorized co-occurrence collaborative signals during the applying phase. Together with the sound theoretical analysis, the extensive experiments conducted on real datasets demonstrate the superiority of the proposed model. The codes and datasets are available on https://github.com/zzhin/CCFCRec.

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“…There are many papers discussing few-shot learning with meta-learning [12], data augmentation [53] or regularization [50], but most of them don't consider the graph data. Furthermore, there have been several methods for few-shot node classification [20,38,56] and few-shot link prediction [10,23,32,43], but they only focus on node-level embedding. Recently, Chauhan et al [7] proposed few-shot graph classification based on graph spectral measures and got satisfactory performance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

Yang

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in the graph learning community. Although Graph Neural Networks (GNNs) have been successfully applied to graph classification tasks, most of them overlook the scarcity of labeled graph data in many applications. For example, in bioinformatics, obtaining protein graph labels usually needs laborious experiments. Recently, few-shot learning has been explored to alleviate this problem with only a few labeled graph samples of test classes. The shared sub-structures between training classes and test classes are essential in the few-shot graph classification. Existing methods assume that the test classes belong to the same set of super-classes clustered from training classes. However, according to our observations, the label spaces of training classes and test classes usually do not overlap in a real-world scenario. As a result, the existing methods don't well capture the local structures of unseen test classes. To overcome the limitation, in this paper, we propose a direct method to capture the sub-structures with a well initialized meta-learner within a few adaptation steps. More specifically, (1) we propose a novel framework consisting of a graph meta-learner, which uses GNNs based modules for fast adaptation on graph data, and a step controller for the robustness and generalization of meta-learner; (2) we provide quantitative analysis for the framework and give a graph-dependent upper bound of the generalization error based on our framework; (3) the extensive experiments on real-world datasets demonstrate that our framework gets state-of-the-art results on several few-shot graph classification tasks compared to baselines.

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Warm Up Cold-start Advertisements

Cited by 110 publications

References 34 publications

Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up

Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up

Contrastive Collaborative Filtering for Cold-Start Item Recommendation

Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

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