Session 7: Cold Start

Date: Wednesday October 16, 09:30 AM – 10:25 AM (GMT+2)
Room: Petruzzelli Theater
Session Chair: Linas Baltrunas

• RES 🕓5Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction
  by Wenhao Li (Huazhong University of Science and Technology; Meituan), Jie Zhou (Beihang University), Chuan Luo (Beihang University), Chao Tang (Meituan), Kun Zhang (Meituan) and Shixiong Zhao (The University of Hong Kong)

  In the realm of modern mobile E-commerce, providing users with nearby commercial service recommendations through location-based online services has become increasingly vital. While machine learning approaches have shown promise in multi-scene recommendation, existing methodologies often struggle to address cold-start problems in unprecedented scenes: the increasing diversity of commercial choices, along with the short online lifespan of scenes, give rise to the complexity of effective recommendations in online and dynamic scenes. In this work, we propose Scene-wise Adaptive Network (SwAN 1), a novel approach that emphasizes high-performance cold-start online recommendations for new scenes. Our approach introduces several crucial capabilities, including scene similarity learning, user-specific scene transition cognition, scene-specific information construction for the new scene, and enhancing the diverged logical information between scenes. We demonstrate SwAN’s potential to optimize dynamic multi-scene recommendation problems by effectively online handling cold-start recommendations for any newly arrived scenes. More encouragingly, SwAN has been successfully deployed in Meituan’s online catering recommendation service, which serves millions of customers per day, and SwAN has achieved a 5.64% CTR index improvement relative to the baselines and a 5.19% increase in daily order volume proportion.

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• RES 🕓5A multimodal single-branch embedding network for recommendation in cold-start and missing modality scenarios
  by Christian Ganhör (Johannes Kepler University Linz), Marta Moscati (Johannes Kepler University Linz), Anna Hausberger (Johannes Kepler University Linz), Shah Nawaz (Johannes Kepler University Linz) and Markus Schedl (Johannes Kepler University Linz; Linz Institute of Technology)

  Most recommender systems adopt collaborative filtering (CF) and provide recommendations based on past collective interactions. Therefore, the performance of CF algorithms degrades when few or no interactions are available, a scenario referred to as cold-start. To address this issue, previous work relies on models leveraging both collaborative data and side information on the users or items. Similar to multimodal learning, these models aim at combining collaborative and content representations in a shared embedding space. In this work we propose a novel technique for multimodal recommendation, relying on a multimodal Single-Branch embedding network for Recommendation (SiBraR). Leveraging weight-sharing, SiBraR encodes interaction data as well as multimodal side information using the same single-branch embedding network on different modalities. This makes SiBraR effective in scenarios of missing modality, including cold start. Our extensive experiments on large-scale recommendation datasets from three different recommendation domains (music, movie, and e-commerce) and providing multimodal content information (audio, text, image, labels, and interactions) show that SiBraR significantly outperforms CF as well as state-of-the-art content-based RSs in cold-start scenarios, and is competitive in warm scenarios. We show that SiBraR’s recommendations are accurate in missing modality scenarios, and that the model is able to map different modalities to the same region of the shared embedding space, hence reducing the modality gap.

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• RES 🕓15A Multi-modal Modeling Framework for Cold-start Short-video Recommendation
  by Gaode Chen (Kuaishou Technology), Ruina Sun (Kuaishou Technology), Yuezihan Jiang (Kuaishou Technology), Jiangxia Cao (Kuaishou Technology), Qi Zhang (Kuaishou Technology), Jingjian Lin (Kuaishou Technology), Han Li (Kuaishou Technology), Kun Gai (Kuaishou Technology) and Xinghua Zhang (Chinese Academy of Sciences)

  Short video has witnessed rapid growth in the past few years in multimedia platforms. To ensure the freshness of the videos, platforms receive a large number of user-uploaded videos every day, making collaborative filtering-based recommender methods suffer from the item cold-start problem (e.g., the new-coming videos are difficult to compete with existing videos). Consequently, increasing efforts tackle the cold-start issue from the content perspective, focusing on modeling the multi-modal preferences of users, a fair way to compete with new-coming and existing videos. However, recent studies ignore the existing gap between multi-modal embedding extraction and user interest modeling as well as the discrepant intensities of user preferences for different modalities. In this paper, we propose M3CSR, a multi-modal modeling framework for cold-start short video recommendation. Specifically, we preprocess content-oriented multi-modal features for items and obtain trainable category IDs by performing clustering. In each modality, we combine modality-specific cluster ID embedding and the mapped original modality feature as modality-specific representation of the item to address the gap. Meanwhile, M3CSR measures the user modality-specific intensity based on the correlation between modality-specific interest and behavioral interest and employs pairwise loss to further decouple user multi-modal interests. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed model. The framework has been deployed on a billion-user scale short video application and has shown improvements in various commercial metrics within cold-start scenarios.

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• RES 🕓15MARec: Metadata Alignment for cold-start Recommendation
  by Julien Monteil (Amazon), Volodymyr Vaskovych (Amazon), Wentao Lu (Amazon), Anirban Majumder (Amazon) and Anton van den Hengel (University of Adelaide)

  For many recommender systems, the primary data source is a historical record of user clicks. The associated click matrix is often very sparse, as the number of users × products can be far larger than the number of clicks. Such sparsity is accentuated in cold-start settings, which makes the efficient use of metadata information of paramount importance. In this work, we propose a simple approach to address cold-start recommendations by leveraging content metadata, Metadata Alignment for cold-start Recommendation (MARec). We show that this approach can readily augment existing matrix factorization and autoencoder approaches, enabling a smooth transition to top performing algorithms in warmer set-ups. Our experimental results indicate three separate contributions: first, we show that our proposed framework largely beats SOTA results on 4 cold-start datasets with different sparsity and scale characteristics, with gains ranging from +8.4% to +53.8% on reported ranking metrics; second, we provide an ablation study on the utility of semantic features, and proves the additional gain obtained by leveraging such features ranges between +46.8% and +105.5%; and third, our approach is by construction highly competitive in warm set-ups, and we propose a closed-form solution outperformed by SOTA results by only 0.8% on average.

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• RES 🕓15Prompt Tuning for Item Cold-start Recommendation
  by Yuezihan Jiang (Kuaishou Technology), Gaode Chen (Kuaishou Technology), Wenhan Zhang (Peking University), Jingchi Wang (Peking University), Yinjie Jiang (Kuaishou Technology), Qi Zhang (Kuaishou Technology), Jingjian Lin (Kuaishou Technology), Peng Jiang (Kuaishou Technology) and Kaigui Bian (Peking University)

  The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones. Prompt learning, a powerful technique used in natural language processing (NLP) to address zero- or few-shot problems, has been adapted for recommender systems to tackle similar challenges. However, existing methods typically rely on content-based properties or text descriptions for prompting, which we argue may be suboptimal for cold-start recommendations due to 1) semantic gaps with recommender tasks, 2) model bias caused by warm-up items contribute most of the positive feedback to the model, which is the core of the cold-start problem that hinders the recommender quality on cold-start items. We propose to leverage high-value positive feedback, termed pinnacle feedback as prompt information, to simultaneously resolve the above two problems. We experimentally prove that compared to the content description proposed in existing works, the positive feedback is more suitable to serve as prompt information by bridging the semantic gaps. Besides, we propose item-wise personalized prompt networks to encode pinnaclce feedback to relieve the model bias by the positive feedback dominance problem. Extensive experiments on four real-world datasets demonstrate the superiority of our model over state-of-the-art methods. Moreover, PROMO has been successfully deployed on a popular short-video sharing platform, a billion-user scale commercial short-video application, achieving remarkable performance gains across various commercial metrics within cold-start scenarios.

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