Social-aware Sparse Attention Network for Session-based Social Recommendation

Challenge:	Existing methods for predicting the next item for an anonymous session do not capture user preferences and noisy irrelevant interactions.
Approach:	They propose to use social networks and historical sessions to provide personalized recommendations for the current session.
Outcome:	The proposed model outperforms existing models on two benchmark datasets.

Similar Papers

Overview

Challenge

Approach

Outcome

Multi-Graph Co-Training for Capturing User Intent in Session-based Recommendation (2025.coling-main)

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Zhe Yang, Tiantian Liang

Multimodality

Challenge:	Existing methods rely on user actions within the current session, overlooking the wealth of auxiliary information available.
Approach:	They propose a session-based recommendation model that leverages the current session graph and similar session graphs to capture the intrinsic relationships between items.
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Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems (2024.lrec-main)

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Xiangping Zheng, Bo Wu, Alex X. Zhang, Wei Li

Responsible & Trustworthy NLP

Challenge:	Presently, graph-based recommendations are limited by session dependencies and data sparsity in real-world scenarios.
Approach:	They propose a method which uses multi-collaborative self-supervised learning in hypergraph neural networks to model item transitions and to mitigate the challenges of data sparsity.
Outcome:	The proposed method outperforms existing methods in a number of domains and consistently outperformed existing methods.

Be More with Less: Hypergraph Attention Networks for Inductive Text Classification (2020.emnlp-main)

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Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, Huan Liu

Information Extraction & Text Mining

Challenge:	Text classification is a critical research topic with broad applications in natural language processing. graph neural networks (GNNs) have received increasing attention but their performance is jeopardized in practice.
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S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation (2022.aacl-main)

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Chen Liang, Jing Xu, Yangkun Lin, Chong Yang, Yongliang Wang

Multimodality

Challenge:	Emotion recognition in conversation (ERC) is a task arousing increasing interest in many fields.
Approach:	They propose a novel GNN-based ERC model that captures speaker and position information.
Outcome:	The proposed model captures speaker and position-aware conversation structure information.

MINER: Multi-Interest Matching Network for News Recommendation (2022.findings-acl)

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Jian Li, Jieming Zhu, Qiwei Bi, Guohao Cai, Lifeng Shang, Zhenhua Dong, Xin Jiang, Qun Liu

Representation Learning

Challenge:	Existing methods learn a single user embedding from user’s historical behaviors to represent the reading interest.
Approach:	They propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest.
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CACL: Community-Aware Heterogeneous Graph Contrastive Learning for Social Media Bot Detection (2024.findings-acl)

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Sirry Chen, Shuo Feng, Liang Songsong, Chen-Chen Zong, Jing Li, Piji Li

Semantic Text Processing

Challenge:	Existing methods for social media bot detection neglect community structure and poor model generalization due to the relatively small scale of the dataset.
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You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP (D19-1)

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Marco Del Tredici, Diego Marcheggiani, Sabine Schulte im Walde, Raquel Fernández

Representation Learning

Challenge:	Current approaches to social media modelling ignore the fact that an individual may be part of several communities which are not equally relevant in all communicative situations.
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Outcome:	The proposed model significantly outperforms existing models on three different tasks and is compared with other models.

The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems (2022.lrec-1)

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Daniel Cheng, Kyle Yan, Phillip Keung, Noah A. Smith

Responsible & Trustworthy NLP

Challenge:	Existing studies have examined the impact of recommendation algorithms on how users discover and join online groups, but there are few standardized datasets for generating such models.
Approach:	They propose to use Reddit to build a dataset that can be used to build models of user engagement with online groups.
Outcome:	The proposed model is based on the behavior of subreddits banned in June 2020 as part of Reddit's efforts to stop the dissemination of hate speech.

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)

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Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul

Semantic Text Processing

Challenge:	Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction).
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Outcome:	The proposed model captures both entity and relation features in any given neighborhood and also encapsulates relation clusters and multi-hop relations.

Denoising Attention for Query-aware User Modeling (2024.findings-naacl)

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Elias Bassani, Pranav Kasela, Gabriella Pasi

Challenge:	Recent work has proposed to build user models at query time by leveraging the Attention mechanism, which allows weighing the contribution of the user-related information w.r.t. the current query.
Approach:	They propose to use the Attention mechanism to build user models at query time by weighing the contribution of the user-related information w.r.t. the Attention variant adopts a robust normalization scheme and introduces . filtering mechanism to better discern among the user related data those helpful for personalization.
Outcome:	The proposed approach improves MAP, MRR, and NDCG above 15% w.r.t. other Attention variants at the state-of-the-art.