HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace

He, Yunzhong; Zhang, Cong; Kong, Ruoyan; Kulkarni, Chaitanya; Liu, Qing; Gandhe, Ashish; Nithianandan, Amit; Arul, Prakash,

doi:10.1145/3543873.3584622

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…This result shows that user contexts could be used in improving reading estimation. Besides user behavioral patterns, other potential contexts to be considered included user's intent [17] (e.g., search queries [11,12]), preference (e.g., user's preference for movie genres [32,33]), item's features (e.g., product ratings [15,31], movies tags [2]).…”

Section: Discussionmentioning

confidence: 99%

Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models

Kong,

Sun,

Zhang

et al. 2023

2023 Symposium on Eye Tracking Research and Applications

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A single digital newsletter usually contains many messages (regions). Users' reading time spent on, and read level (skip/skim/read-in-detail) of each message is important for platforms to understand their users' interests, personalize their contents, and make recommendations. Based on accurate but expensive-to-collect eyetracker-recorded data, we built models that predict per-region reading time based on easy-to-collect Javascript browser tracking data.With eye-tracking, we collected 200k ground-truth datapoints on participants reading news on browsers. Then we trained machine learning and deep learning models to predict message-level reading time based on user interactions like mouse position, scrolling, and clicking. We reached 27% percentage error in reading time estimation with a two-tower neural network based on user interactions only, against the eye-tracking ground truth data, while the heuristic baselines have around 46% percentage error. We also discovered the benefits of replacing per-session models with per-timestamp models, and adding user pattern features. We concluded with suggestions on developing message-level reading estimation techniques based on available data. CCS Concepts: • Human-centered computing → Collaborative and social computing systems and tools; Empirical studies in collaborative and social computing.

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Section: Discussionmentioning

confidence: 99%

Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models

Kong,

Sun,

Zhang

et al. 2023

2023 Symposium on Eye Tracking Research and Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…For instance, Liu et al [23] proposed a mixture of conventional neural network and Naive Bayes as a classifier [23] while Wang et al [36] incorporated the hierarchy of label information by a graph encoder into the text encoder [36]. Besides, the context-aware session information [5] and searcher engagement data [14] are explored as well. Different from these existing efforts relying on extra information or overlooking abundant unlabeled data, we aim to boost performance using only easily accessible query and label data combined with unlabeled queries.…”

Section: Hierarchical Query Classificationmentioning

confidence: 99%

Hierarchical Query Classification in E-commerce Search

He,

Nag,

Cui

et al. 2024

Companion Proceedings of the ACM Web Conference 2024

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E-commerce platforms typically store and structure product information and search data in a hierarchy. Efficiently categorizing user search queries into a similar hierarchical structure is paramount in enhancing user experience on e-commerce platforms as well as news curation and academic research. The significance of this task is amplified when dealing with sensitive query categorization or critical information dissemination, where inaccuracies can lead to considerable negative impacts. The inherent complexity of hierarchical query classification is compounded by two primary challenges: (1) the pronounced class imbalance that skews towards dominant categories, and (2) the inherent brevity and ambiguity of search queries that hinder accurate classification.To address these challenges, we introduce a novel framework that leverages hierarchical information through (i) enhanced representation learning that utilizes the contrastive loss to discern finegrained instance relationships within the hierarchy, called "instance hierarchy", and (ii) a nuanced hierarchical classification loss that attends to the intrinsic label taxonomy, named "label hierarchy". Additionally, based on our observation that certain unlabeled queries share typographical similarities with labeled queries, we propose a neighborhood-aware sampling technique to intelligently select these unlabeled queries to boost the classification performance. Extensive experiments demonstrate that our proposed method is better than state-of-the-art (SOTA) on the proprietary Amazon dataset, and comparable to SOTA on the public datasets of Web of Science and RCV1-V2. These results underscore the efficacy of our proposed solution, and pave the path toward the next generation of hierarchy-aware query classification systems.

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Comprehensive Transformer-Based Model Architecture for Real-World Storm Prediction

Lin,

Yuan,

Zhang

et al. 2023

Lecture Notes in Computer Science

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HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace

Cited by 4 publications

References 8 publications

Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models

Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models

Hierarchical Query Classification in E-commerce Search

Comprehensive Transformer-Based Model Architecture for Real-World Storm Prediction

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