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20172023
most citedGlobal Context Enhanced Graph Neural Networks for Session-based Recommendation

554 citations · 1k across the 22 of their papers we have counts for

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13 papers · 1 filter

cs.IR2022223 cited

Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

Ding Zou, Wei Wei, Xian-Ling Mao +4

Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-awa…

cs.IR2021554 cited

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

Ziyang Wang, Wei Wei, Gao Cong +3

Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Almost all the existing solutions for SBR model us…

cs.IR2020

Exploring Global Information for Session-based Recommendation

Ziyang Wang, Wei Wei, Gao Cong +4

Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Most existing SBR studies model the user preferenc…

cs.IR20206 cited

FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval

Dehong Gao, Linbo Jin, Ben Chen +5

In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry. Different from the matching in the general domain, the fashion matching is r…

cs.IR202082 cited

Open-Retrieval Conversational Question Answering

Chen Qu, Liu Yang, Cen Chen +3

Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and convers…

cs.IR20203 cited

IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

Liu Yang, Minghui Qiu, Chen Qu +5

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarifi…