Multi-task Ranking with User Behaviors for Text-video Search

Liu, Peidong; Liao, Dongliang; Wang, Jinpeng; Wu, Yangxin; Li, Gongfu; Xia, Shu-Tao; Xu, Jin

doi:10.1145/3487553.3524207

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Alternative measures such as precision [105,106], recall [107,108], and F1-score [109,110] are used to evaluate model performance, particularly when dealing with imbalanced data. Additionally, evaluation measures like the area under the curve (AUC) [111,112] and receiver operating characteristic (ROC) [10,113] curve are frequently used to assess binary classifiers. These measures provide insights into the model's ability to differentiate between positive and negative instances, particularly when the costs associated with false positives and false negatives differ.…”

Section: Quality Models and Evaluation Measuresmentioning

confidence: 99%

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Farshidi,

Rezaee,

Mazaheri

et al. 2023

Preprint

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Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user’s request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks.

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Section: Quality Models and Evaluation Measuresmentioning

confidence: 99%

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Farshidi,

Rezaee,

Mazaheri

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Understanding user intent modeling for conversational recommender systems: a systematic literature review

Farshidi,

Rezaee,

Mazaheri

et al. 2024

User Model User-Adap Inter

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User intent modeling in natural language processing deciphers user requests to allow for personalized responses. The substantial volume of research (exceeding 13,000 publications in the last decade) underscores the significance of understanding prevalent models in AI systems, with a focus on conversational recommender systems. We conducted a systematic literature review to identify models frequently employed for intent modeling in conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Furthermore, we conducted two case studies to assess the utility of our proposed decision model in guiding research modelers in selecting user intent modeling models for developing their conversational recommender systems. Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. The study offers practical insights into the domain of user intent modeling, specifically enhancing the development of conversational recommender systems. The introduced decision model provides a structured framework, enabling researchers to navigate the selection of the most apt intent modeling methods for conversational recommender systems.

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A holistic view on positive and negative implicit feedback for micro-video recommendation

Gu,

2024

Knowledge-Based Systems

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Multi-task Ranking with User Behaviors for Text-video Search

Cited by 3 publications

References 10 publications

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Understanding user intent modeling for conversational recommender systems: a systematic literature review

A holistic view on positive and negative implicit feedback for micro-video recommendation

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