Modeling User Behavior with Graph Convolution for Personalized Product Search

Lü, Fan; Li, Qimai; Liu, Bo; Wu, Xiao-Ming; Zhang, Xiaolin; Lv, Fuyu; Lin, Guli; Li, Sen; Yang, Keping

doi:10.1145/3485447.3511949

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Users visit these platforms and enter search queries to retrieve their desired items [11,20]. Therefore, matching the query to the relevant items is essential for the success of e-commerce platforms [7]. To address the challenge of semantic alignment between queries and items, several works attempted to incorporate contextual information [12,32] to obtain more comprehensive representations.…”

Section: Related Workmentioning

confidence: 99%

Multi-Intent Attribute-Aware Text Matching in Searching

Li,

Chen,

Xiang

et al. 2024

Proceedings of the 17th ACM International Conference on Web Search and Data Mining

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Text matching systems have become a fundamental service in most Searching platforms. For instance, they are responsible for matching user queries to relevant candidate items, or rewriting the user-input query to a pre-selected high-performing one for a better search experience. In practice, both the queries and items often contain multiple attributes, such as the category of the item and the location mentioned in the query, which represent condensed key information that is helpful for matching. However, most of the existing works downplay the effectiveness of attributes by integrating them into text representations as supplementary information. Hence, in this work, we focus on exploring the relationship between the attributes from two sides. Since attributes from two ends are often not aligned in terms of number and type, we propose to exploit the benefit of attributes by multiple-intent modeling. The intents extracted from attributes summarize the diverse needs of queries and provide rich content of items, which are more refined and abstract, and can be aligned for paired inputs. Concretely, we propose a multi-intent attribute-aware matching model (MIM), which consists of three main components: attribute-aware encoder, multiintent modeling, and intent-aware matching. In the attribute-aware encoder, the text and attributes are weighted and processed through a scaled attention mechanism with regard to the attributes' importance. Afterward, the multi-intent modeling extracts intents from two ends and aligns them. Herein, we come up with a distribution loss to ensure the learned intents are diverse but concentrated, and a kullback-leibler divergence loss that aligns the learned intents. Finally, in the intent-aware matching, the intents are evaluated by a self-supervised masking task, and then incorporated to output the final matching result. Extensive experiments on three real-world

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Section: Related Workmentioning

confidence: 99%

Multi-Intent Attribute-Aware Text Matching in Searching

Li,

Chen,

Xiang

et al. 2024

Proceedings of the 17th ACM International Conference on Web Search and Data Mining

View full text Add to dashboard Cite

show abstract

“…Table 6 provides an overview of these datasets and their frequency of usage from 2005 to 2023. Notably, TREC, MovieLens, Amazon, Yelp, and AOL emerged as the top five datasets commonly used in evaluating intent modeling approaches for recommender systems [10,122,123] and search engines [6,124,125]. These datasets have been utilized in over 200 publications, highlighting their significance and wide adoption in the field.…”

Section: Datasetsmentioning

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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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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Modeling User Behavior with Graph Convolution for Personalized Product Search

Cited by 8 publications

References 25 publications

Multi-Intent Attribute-Aware Text Matching in Searching

Multi-Intent Attribute-Aware Text Matching in Searching

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

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

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