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- research-articleAugust 2024
When Box Meets Graph Neural Network in Tag-aware Recommendation
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 1770–1780https://doi.org/10.1145/3637528.3671973Last year has witnessed the re-flourishment of tag-aware recommender systems supported by the LLM-enriched tags. Unfortunately, though large efforts have been made, current solutions may fail to describe the diversity and uncertainty inherent in user ...
- research-articleAugust 2024
Dataset Regeneration for Sequential Recommendation
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 3954–3965https://doi.org/10.1145/3637528.3671841The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users. Significant efforts have been made to enhance the capabilities of SR systems. These methods typically ...
- research-articleAugust 2024
SiGeo: Sub-One-Shot NAS via Geometry of Loss Landscape
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 4536–4547https://doi.org/10.1145/3637528.3671712Neural Architecture Search (NAS) has become a widely used tool for automating neural network design. While one-shot NAS methods have successfully reduced computational requirements, they often require extensive training. On the other hand, zero-shot NAS ...
- research-articleAugust 2024
Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 5669–5679https://doi.org/10.1145/3637528.3671618Given an input query, a recommendation model is trained using user feedback data (e.g., click data) to output a ranked list of items. In real-world systems, besides accuracy, an important consideration for a new model is novelty of its top-k ...
- research-articleAugust 2024
Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 5116–5125https://doi.org/10.1145/3637528.3671601Lifelong sequential modeling (LSM) has significantly advanced recommendation systems on social media platforms. Diverging from single-domain LSM, cross-domain LSM involves modeling lifelong behavior sequences from a source domain to a different target ...
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- ArticleAugust 2024
IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model
Knowledge Science, Engineering and ManagementPages 270–282https://doi.org/10.1007/978-981-97-5489-2_24AbstractFaced with the burgeoning volume of academic literature, researchers often need help with uncertain article quality and mismatches in term searches using traditional academic engines. We introduce IntellectSeeker, an innovative and personalized ...
- ArticleAugust 2024
Data Augmentation Integrating User Preferences for Sequential Recommendation
Advanced Intelligent Computing Technology and ApplicationsPages 467–477https://doi.org/10.1007/978-981-97-5615-5_38AbstractIn order to effectively alleviate the data sparsity issue, the application of contrastive learning in sequential recommendation is studied. To address the problem of noise introduced by random data augmentation, the data augmentation method ...
- research-articleJuly 2024
EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 698–708https://doi.org/10.1145/3626772.3657890In recent years, short video platforms have gained widespread popularity, making the quality of video recommendations crucial for retaining users. Existing recommendation systems primarily rely on behavioral data, which faces limitations when inferring ...
- research-articleJuly 2024
Deep Pattern Network for Click-Through Rate Prediction
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 1189–1199https://doi.org/10.1145/3626772.3657777Click-through rate (CTR) prediction plays a pivotal role in real-world applications, particularly in recommendation systems and online advertising. A significant research branch in this domain focuses on user behavior modeling. Current research ...
- research-articleJuly 2024
ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit
- Wenjie Li,
- Zhongren Wang,
- Jinpeng Wang,
- Shu-Tao Xia,
- Jile Zhu,
- Mingjian Chen,
- Jiangke Fan,
- Jia Cheng,
- Jun Lei
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 1763–1773https://doi.org/10.1145/3626772.3657763As an emerging privacy-preserving approach to leveraging cross-platform user interactions, vertical federated learning (VFL) has been increasingly applied in recommender systems. However, vanilla VFL is only applicable to overlapped users, ignoring ...
- research-articleMay 2024
Decoding YouTube's Recommendation System: A Comparative Study of Metadata and GPT-4 Extracted Narratives
WWW '24: Companion Proceedings of the ACM Web Conference 2024Pages 1468–1472https://doi.org/10.1145/3589335.3651913YouTube's recommendation system is integral to shaping user experiences by suggesting content based on past interactions using collaborative filtering techniques. Nonetheless, concerns about potential biases and homogeneity in these recommendations are ...
- short-paperMay 2024
Boost Social Recommendation via Adaptive Denoising Network
WWW '24: Companion Proceedings of the ACM Web Conference 2024Pages 577–580https://doi.org/10.1145/3589335.3651473Social recommendation aims to integrate social relationships to improve the performance of recommendation, and has attracted increasing attention in the field of recommendation system. Recently, Graph Neural Networks (GNNs) based methods for social ...
- research-articleMay 2024
AutoML for Large Capacity Modeling of Meta's Ranking Systems
- Hang Yin,
- Kuang-Hung Liu,
- Mengying Sun,
- Yuxin Chen,
- Buyun Zhang,
- Jiang Liu,
- Vivek Sehgal,
- Rudresh Rajnikant Panchal,
- Eugen Hotaj,
- Xi Liu,
- Daifeng Guo,
- Jamey Zhang,
- Zhou Wang,
- Shali Jiang,
- Huayu Li,
- Zhengxing Chen,
- Wen-Yen Chen,
- Jiyan Yang,
- Wei Wen
WWW '24: Companion Proceedings of the ACM Web Conference 2024Pages 374–382https://doi.org/10.1145/3589335.3648336Web-scale ranking systems at Meta serving billions of users is complex. Improving ranking models is essential but engineering heavy. Automated Machine Learning (AutoML) can potentially release engineers from labor intensive work of tuning ranking models; ...
- research-articleMay 2024
MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
WWW '24: Companion Proceedings of the ACM Web Conference 2024Pages 216–225https://doi.org/10.1145/3589335.3648319Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for ...
Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training
WWW '24: Proceedings of the ACM Web Conference 2024Pages 3940–3951https://doi.org/10.1145/3589334.3645702Federated Recommendation (FedRec) systems have emerged as a solution to safeguard users' data in response to growing regulatory concerns. However, one of the major challenges in these systems lies in the communication costs that arise from the need to ...
- research-articleMay 2024
Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization
WWW '24: Proceedings of the ACM Web Conference 2024Pages 3485–3496https://doi.org/10.1145/3589334.3645463Click-Through Rate (CTR) prediction holds paramount significance in online advertising and recommendation scenarios. Despite the proliferation of recent CTR prediction models, the improvements in performance have remained limited, as evidenced by open-...
- research-articleMay 2024
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
WWW '24: Proceedings of the ACM Web Conference 2024Pages 3253–3264https://doi.org/10.1145/3589334.3645371Optimization metrics are crucial for building recommendation systems at scale. However, an effective and efficient metric for practical use remains elusive. While Top-K ranking metrics are the gold standard for optimization, they suffer from significant ...
- research-articleApril 2024
Dialoging Resonance in Human-Chatbot Conversation: How Users Perceive and Reciprocate Recommendation Chatbot's Self-Disclosure Strategy
Proceedings of the ACM on Human-Computer Interaction (PACMHCI), Volume 8, Issue CSCW1Article No.: 200, Pages 1–28https://doi.org/10.1145/3653691Using chatbots to make recommendations is increasingly popular. The design of recommendation chatbots has mainly been taking an information-centric approach by focusing on the recommended content per se. Limited attention is on how social connection and ...
- research-articleAugust 2024
API-Miner: an API-to-API Specification Recommendation Engine
FinanSE '24: Proceedings of the 1st IEEE/ACM Workshop on Software Engineering Challenges in Financial FirmsPages 9–16https://doi.org/10.1145/3643665.3648049When designing a new API for a large project, developers need to make smart design choices so that their code base can grow sustainably. To ensure that new API components are well designed, developers can learn from existing API components. However, the ...
- research-articleJuly 2024
MicroRec: Leveraging Large Language Models for Microservice Recommendation
MSR '24: Proceedings of the 21st International Conference on Mining Software RepositoriesPages 419–430https://doi.org/10.1145/3643991.3644916The increasing adoption of microservices in software development requires effective recommendation systems that guide developers to relevant microservices. In this paper, we introduce MicroRec, a novel microservice recommender framework which leverages ...