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检索条件"任意字段=Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization"

共 94 条记录，以下是1-10 订阅

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Ads Supply personalization via Doubly Robust Learning 24

Ads Supply Personalization via Doubly Robust Learning

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33rd acm International conference on Information and Knowledge Management (CIKM)

作者： Shi, Wei Fu, Chen Xu, Qi Chen, Sanjian Zhang, Jizhe Zhu, Qinqin Hua, Zhigang Yang, Shuang Meta Platforms Inc Sunnyvale CA 94086 USA Meta Platforms Inc Menlo Pk CA USA

ISBN: (纸本)9798400704369

Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-scale system, the challenge for ads supply lies in modeling the counterfactual effects of a conservative supply treatment (e.g., a small density change) over an extended duration. In this paper, we present a streamlined framework for personalized ad supply. This framework optimally utilizes information from data collection policies through the doubly robust learning. Consequently, it significantly improves the accuracy of long-term treatment effect estimates. Additionally, its low-complexity design not only results in computational cost savings compared to existing methods, but also makes it scalable for billion-scale applications. Through both offline experiments and online production tests, the framework consistently demonstrated significant improvements in top-line business metrics over months. The framework has been fully deployed to live traffic in one of the world's largest social media platforms.

关键词： Advertisement Causal Learning Doubly Robust Learning

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Towards Advancing Text-Based user and Item Representation in Personalized Recommendation 24

Towards Advancing Text-Based User and Item Representation in...

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33rd acm International conference on Information and Knowledge Management (CIKM)

作者： Lyu, Hanjia Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9798400704369

In the realm of personalized recommendation systems, accurately capturing user preferences and item characteristics is important for delivering relevant and satisfying recommendations. This study introduces innovative approaches using Large Language Models (LLMs) to generate detailed textual descriptions that enhance both user and item representations. We propose a dual strategy: for user representation, we employ supervised fine-tuning coupled with Retrieval-Augmented Generation (RAG) to keep the model current with dynamic user preferences;for item representation, we leverage the extensive knowledge base of LLMs to enrich item descriptions and infer traits from user interactions. These methods promise a deeper, more nuanced understanding of both users and items, potentially leading to superior recommendation accuracy. We adopt a rigorous evaluation methodology, ensuring the reliability of our results and the effectiveness of our proposed system. This paper discusses these methodologies, presents our preliminary findings, and highlights the potential of text-augmented profiles in advancing recommendation systems.

关键词： personalization user understanding content understanding recommendation representation learning

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GMAP 2024: 3rd Workshop on Group modeling, adaptation and personalization 32

GMAP 2024: 3rd Workshop on Group Modeling, Adaptation and Pe...

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32nd acm conference on user modeling, adaptation and personalization (acm UMAP)

作者： Barile, Francesco Delic, Amra Peska, Ladislav Saccardi, Isabella Vinella, Federica Lucia Maastricht Univ Maastricht Netherlands Univ Sarajevo Sarajevo Bosnia & Herceg Charles Univ Prague Prague Czech Republic Univ Utrecht Utrecht Netherlands

ISBN: (纸本)9798400704666

While most recommender systems cater to individual users' needs, there are numerous situations where these systems are needed to meet groups' demands. These systems are broadly labelled as Group Recommender Systems (GRSys). Traits like interpersonal relationships, group mood, and emotional contagion are essential to fulfilling the group's needs. However, the group's characteristics are frequently ill-defined and dynamic and are typically absent from systems modeling. Moreover, GRSys must maneuver between the needs of the group and the individuals when opinions differ and can contradict each other. The third edition of GMAP proposes consolidating a community of scholars interested in group modeling, adaptation, and personalization. Through the workshop, researchers continue their examination of the difficulties and possibilities of creating efficient procedures and instruments to facilitate collective decision-making. GMAP 2024 offered this unique opportunity to gather scholars from different fields to enrich discussions over GRSys' research. The workshop also allowed attendees to strengthen their networks and establish newconnections conducive to cutting-edge collaborative research.

关键词： Group Recommender Systems Group Formation Explainability Group Psychology

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Dual-level Intents modeling for Knowledge-aware Recommendation 24

Dual-level Intents Modeling for Knowledge-aware Recommendati...

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33rd acm International conference on Information and Knowledge Management (CIKM)

作者： Zeng, Jin Wang, Nan Li, Jinbao Heilongjiang Univ Harbin Peoples R China Qilu Univ Technol Jinan Peoples R China

ISBN: (纸本)9798400704369

Previous user-item interaction graphs have typically focused on simple interaction between users and items, failing to identify the important effects of user's intents in the interaction. While recent studies have ventured into exploring intent relationships between users and items for modeling, they predominantly emphasize user preferences manifesting in the interaction, overlooking knowledge-driven insight, thereby limiting the interpretability of intent. In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel dual-level intents modeling framework called DIM. DIM aims to mine user's true intents, which usually include user popularity preference and personalized preference. Therefore, we extract both the popular and personalized user preferences from attribute tuples within the knowledge graph at the global and local levels, respectively. Experimental results on three datasets demonstrate the superiority of DIM over various state-of-the-art approaches.

关键词： Recommendation Knowledge Graph user Intents

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Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural Networks 24

Quantum Cognition-Inspired EEG-based Recommendation via Grap...

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33rd acm International conference on Information and Knowledge Management (CIKM)

作者： Han, Jinkun Li, Wei Li, Yingshu Cai, Zhipeng Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)9798400704369

Current recommendation systems recommend goods by considering users' historical behaviors, social relations, ratings, and other multi-modals. Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed. With the development of brain-computer interfaces, it is time to explore next-generation recommenders that show users' real-time thoughts without delay. Electroencephalography (EEG) is a promising method of collecting brain signals because of its convenience and mobility. Currently, there is only few research on EEG-based recommendations due to the complexity of learning human brain activity. To explore the utility of EEG-based recommendation, we propose a novel neural network model, QUARK, combining Quantum Cognition Theory and Graph Convolutional Networks for accurate item recommendations. Compared with the state-of-the-art recommendation models, the superiority of QUARK is confirmed via extensive experiments.

关键词： Recommendation System Quantum Cognition Theory Graph Neural Network EEG Data personalization

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WWW 2024 Companion - Companion proceedings of the acm Web conference

WWW 2024 Companion - Companion Proceedings of the ACM Web Co...

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33rd Companion of the acm World Wide Web conference, WWW 2023

ISBN: (纸本)9798400701726

The proceedings contain 341 papers. The topics discussed include: knowledge enhanced multi-intent transformer network for recommendation;collaborative-enhanced prediction of spending on newly downloaded mobile games under consumption uncertainty;large language model based long-tail query rewriting in Taobao search;improving search for new product categories via synthetic query generation strategies;scaling user modeling: large-scale online user representations for ads personalization in meta;a semi-supervised multi-channel graph convolutional network for query classification in e-commerce;Rankitect: ranking architecture search battling world-class engineers at meta scale;modeling user viewing flow using large language models for article recommendation;and counterfactual data augmentation for debiased coupon recommendations based on potential knowledge.

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3rd International Workshop on Industrial Recommendation Systems (IRS) 24

3rd International Workshop on Industrial Recommendation Syst...

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33rd acm International conference on Information and Knowledge Management (CIKM)

作者： Ma, Luyi Li, Xiaohan Ahmadi, Kamilia Xu, Jianpeng Yu, Philip S. Karypis, George Walmart Global Tech Sunnyvale CA 94086 USA Warner Bros Discovery San Francisco CA USA Univ Illinois Chicago IL USA Univ Minnesota Minneapolis MN USA

ISBN: (纸本)9798400704369

Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms, and social networks, to provide suggestions that users will most likely consume or connect, thus improving the user experience [1]. This motivates people in industry and research organizations to focus on personalization and recommendation algorithms, resulting in many research papers [2, 3]. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment, including but not limited to business metric definition and evaluation, scalability, recommendation quality control, robustness, fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions to industrial recommendation systems. This workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue for industrial researchers to share practical insights and for academic researchers to become aware of the additional factors of algorithm adoption in real production systems.

关键词： Industrial Recommendation Systems E-commerce personalization

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Towards Better Utilization of Multiple Views for Bundle Recommendation 24

Towards Better Utilization of Multiple Views for Bundle Reco...

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33rd acm International conference on Information and Knowledge Management (CIKM)

作者： Kim, Kyungho Kim, Sunwoo Lee, Geon Shin, Kijung Korea Adv Inst Sci & Technol Seoul South Korea

ISBN: (纸本)9798400704369

Bundle recommender systems aim to recommend suitable collections (i.e., bundles) of items to each user, meeting their diverse needs with all-in-one convenience. Typically, they utilize three distinct types of information: user-bundle purchase interactions (U-B view), user-item purchase interactions (U-I view), and bundle-item affiliations (B-I view). Our focus is on better integrating these three perspectives (i.e., views) to deliver more accurate bundle recommendations. Our examination of different role (main or sub-views) combinations of the views reveals two key observations: (1) the best combination varies across target users (i.e., who receive recommendations), and (2) the U-I view is relatively weak as the main role. Driven by these observations, we propose PET, which synergizes the three views through (1) personalized view weighting, (2) U-I view enhancement, and (3) two-pronged contrastive learning. Our extensive experiments demonstrate that PET significantly outperforms existing methods in all popular benchmark datasets. Our code and datasets are available at https://***/K-Kyungho/PET.

关键词： Bundle Recommendation Multi-view Fusion personalization

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Missing Interest modeling with Lifelong user Behavior Data for Retrieval Recommendation 24

Missing Interest Modeling with Lifelong User Behavior Data f...

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33rd acm International conference on Information and Knowledge Management (CIKM)

作者： Chen, Gaode Jiang, Yuezihan Huang, Rui Cai, Kuo Luo, Yunze Sun, Ruina Zhang, Qi Li, Han Gai, Kun Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9798400704369

Rich user behavior data has been proven to be of great value for recommendation systems. modeling lifelong user behavior data in the retrieval stage to explore user long-term preference and obtain comprehensive retrieval results is crucial. Existing lifelong modeling methods cannot applied to the retrieval stage because they extract target-relevant items through the coupling between the user and the target item. Moreover, the current retrieval methods fail to precisely capture user interests when the length of the user behavior sequence increases further. That leads to a gap in the ability of retrieval models to model lifelong user behavior data. In this paper, we propose the concept of missing interest, leveraging the idea of complementarity, which serves as a supplement to short-term interest based on lifelong behavior data in the retrieval stage. Specifically, we design a missing interest operator and deploy it in Kafka data stream, without incurring latency or storage costs. This operator derives categories and authors of items that the user was previously interested in but has recently missed, and uses these as triggers to output missing features to the downstream retrieval model. Our retrieval model is a complete dual-tower structure that combines short-term and missing interests on the user side to provide a comprehensive depiction of lifelong behaviors. Since 2023, the presented solution has been deployed in Kuaishou, one of the most popular short-video streaming platforms in China with hundreds of millions of active users.

关键词： Recommender System Retrieval user Interest modeling Long Sequential user Behavior Data

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Towards Graph Foundation Models for personalization 24

Towards Graph Foundation Models for Personalization

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33rd Companion of the acm World Wide Web conference, WWW 2023

作者： Damianou, Andreas Fabbri, Francesco Gigioli, Paul De Nadai, Marco Wang, Alice Palumbo, Enrico Lalmas, Mounia Spotify United Kingdom Spotify Spain Spotify United States Spotify Denmark Spotify Italy

ISBN: (纸本)9798400701726

In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build state-of-the-art solutions. In this regard, two of the biggest trends in research around this subject are Graph Neural Networks (GNNs) and Foundation Models (FMs). While GNNs emerged as a popular solution in industry for powering personalization at scale, FMs have only recently caught attention for their promising performance in personalization tasks like ranking and retrieval. In this paper, we present a graph-based foundation modeling approach tailored to personalization. Central to this approach is a Heterogeneous GNN (HGNN) designed to capture multi-hop content and consumption relationships across a range of recommendable item types. To ensure the generality required from a Foundation Model, we employ a Large Language Model (LLM) text-based featurization of nodes that accommodates all item types, and construct the graph using co-interaction signals, which inherently transcend content specificity. To facilitate practical generalization, we further couple the HGNN with an adaptation mechanism based on a two-tower (2T) architecture, which also operates agnostically to content type. This multi-stage approach ensures high scalability;while the HGNN produces general purpose embeddings, the 2T component models in a continuous space the sheer size of user-item interaction data. Our comprehensive approach has been rigorously tested and proven effective in delivering recommendations across a diverse array of products within a real-world, industrial audio streaming platform. © 2024 Copyright held by the owner/author(s). Publication rights licensed to acm.

关键词： Graphic methods

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