FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation

Sun, Xuehan; Shi, Tianyao; Gao, Xiaofeng; Kang, Yanrong; Chen, Guihai

doi:10.1145/3404835.3462831

Cited by 15 publications

(3 citation statements)

References 34 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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“…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%

“…Since our methods only act on item ID embedding, our method is compatible with different kinds of recommendation backbone models. Moreover, as the proposed method only utilizes the side information of the items which is available in most real-world scenarios, it can be easily applied in large-scale industrial systems without further development of data or training pipeline, such as item-item relationship required by graph embedding learning methods [19,25] or separation of global and local update in meta-learning method [35]. We evaluate the proposed method by extensive offline experiments on public datasets and online A/B tests on a large-scale real-world recommendation platform.…”

Section: Introductionmentioning

confidence: 99%

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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