Search Results for author: Qi Shen

Found 10 papers, 3 papers with code

Geo-BERT Pre-training Model for Query Rewriting in POI Search

no code implementations Findings (EMNLP) 2021 Xiao Liu, Juan Hu, Qi Shen, Huan Chen

Finally, we train a BERT-like pre-training model with text and POIs’ graph embeddings to get an integrated representation of both geographic and semantic information, and apply it in the QR of POI search.

Graph Representation Learning

Data-Augmented Counterfactual Learning for Bundle Recommendation

no code implementations19 Oct 2022 Shixuan Zhu, Qi Shen, Yiming Zhang, Zhenwei Dong, Zhihua Wei

In this paper, we propose a novel graph learning paradigm called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the impact of data sparsity problem and improve bundle recommendation.

Data Augmentation Graph Learning +1

Temporal aware Multi-Interest Graph Neural Network For Session-based Recommendation

no code implementations31 Dec 2021 Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Zhihua Wei

Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences.

Session-Based Recommendations

Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation

1 code implementation31 Dec 2021 Chuan Cui, Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Hanning Gao, Zhihua Wei

Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation.

Session-Based Recommendations

Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation

1 code implementation22 Dec 2021 Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long, Jian Pei

As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances.

Multiple-choice

Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation

no code implementations24 Sep 2021 Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long

In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation.

Graph Learning

Multi-behavior Graph Contextual Aware Network for Session-based Recommendation

no code implementations24 Sep 2021 Qi Shen, Lingfei Wu, Yitong Pang, Yiming Zhang, Zhihua Wei, Fangli Xu, Bo Long

Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii).

Session-Based Recommendations

Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

1 code implementation8 Jul 2021 Yitong Pang, Lingfei Wu, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long, Jian Pei

Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions.

Session-Based Recommendations

Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection

no code implementations13 Oct 2020 Qi Shen, Shengjie Zhao, Rongqing Zhang, Bin Zhang

The drowsiness detection system is trained and evaluated on the famous Nation Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset and we obtain an accuracy of 94. 46%, which outperforms most existing fatigue detection models.

Action Detection Image Classification +1

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