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An Enhanced Multi-modal Recommendation Based on Alternate Training with Knowledge Graph Representation

Yuequn Wang, Liyan Dong, Hao Zhang, Xintao Ma, Yongli Li, Minghui Sun
2020 IEEE Access  
It is common to use knowledge graphs as multi-modal information in recommendation systems.  ...  In this paper, we use a knowledge graph (KG), user attributes and item attributes as multi-modal information to make deep multi-modal recommendations.  ... 
doi:10.1109/access.2020.3039388 fatcat:zhvcqjxcdzgvxkrqjzp53peuue

Multi-modal Network Representation Learning

Chuxu Zhang, Meng Jiang, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla
2020 Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining  
Therefore, automating the feature discovery through representation learning in multi-modal networks has become essential for many applications.  ...  In today's information and computational society, complex systems are often modeled as multi-modal networks associated with heterogeneous structural relation, unstructured attribute/content, temporal context  ...  INTRODUCTION Complex systems such as social media, cybersecurity system, or chemical synthesis, are often modeled as multi-modal networks.  ... 
doi:10.1145/3394486.3406475 fatcat:vbnikhs53ndczblj2nepa5nq2y

Recent Advances in Heterogeneous Relation Learning for Recommendation [article]

Chao Huang
2021 arXiv   pre-print
To address this problem, recent research developments can fall into three major lines: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation.  ...  We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks, for effectively distilling heterogeneous contextual information.  ...  Graph-based Multi-Behavior Recommender Systems. The second type of multi-behavior recommender systems are built upon the graph neural architectures.  ... 
arXiv:2110.03455v1 fatcat:fskj4qdsibfnxefklazdli3tgu

Cross-Domain Recommendation via Clustering on Multi-Layer Graphs

Aleksandr Farseev, Ivan Samborskii, Andrey Filchenkov, Tat-Seng Chua
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
In addition, we suggest a new approach for automatic construction of inter-network relationship graph based on the data, which eliminates the necessity of having pre-defined domain knowledge.  ...  Another approach to achieve higher recommendation results is to utilize group knowledge, which is able to diversify recommendation output.  ...  Another aspect which lacks research, and is worth exploring, is the role of personal and group knowledge integration in cross-source multi-modal recommendation.  ... 
doi:10.1145/3077136.3080774 dblp:conf/sigir/FarseevSFC17 fatcat:oq6h6njzmffj7lhzub2nsfor6a

A multimedia recommendation model based on collaborative graph [article]

Breda Lim, Shubhi Bansal, Ahmed Buru, Kayla Manthey
2022 arXiv   pre-print
neural network in the recommendation system is improved by using an attention mechanism to fuse the multi-layer state output information, allowing the shallow structural features provided by the intermediate  ...  As one of the main solutions to the information overload problem, recommender systems are widely used in daily life.  ...  [35] further combined multi modal and knowledge graphs together, and proposed a multimodal graph attention technique to propagate information over a multimodal knowledge graph (MMKG) and use the obtained  ... 
arXiv:2205.14931v1 fatcat:yfolhph7i5bg7ihtqhzfl3rhoa

BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer [article]

N. Nikzad-Khasmakhi, M. A. Balafar, M.Reza Feizi-Derakhshi, Cina Motamed
2020 arXiv   pre-print
In this paper, we introduce a multimodal classification approach for expert recommendation system (BERTERS).  ...  In our proposed system, the modalities are derived from text (articles published by candidates) and graph (their co-author connections) information.  ...  Conclusion In this paper, a multimodal classification approach, called BERTERS, has been proposed for expert recommendation system.  ... 
arXiv:2007.07229v1 fatcat:pknabu6xqrfqndyp6dsmkd2gye

Multi-Modal Contrastive Pre-training for Recommendation

Zhuang Liu, Yunpu Ma, Matthias Schubert, Yuanxin Ouyang, Zhang Xiong
2022 Proceedings of the 2022 International Conference on Multimedia Retrieval  
For items, we consider three modalities: description text, images, and item graph. Moreover, the description text and image complement each other for the same item.  ...  For users, we propose intramodal aggregation and inter-modal aggregation to fuse review texts and the structural information of the user graph.  ...  The authors of this work take full responsibilities for its content. We thank the anonymous reviewers for their insightful comments and suggestions on this paper.  ... 
doi:10.1145/3512527.3531378 fatcat:xobr4bdp4bcsna4kixe6eneeia

LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs [article]

Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Kang Liu, Bin Sun, Shutao Li, Jun Zhao
2022 arXiv   pre-print
This paper presents a medical conversational question answering (CQA) system based on the multi-modal knowledge graph, namely "LingYi", which is designed as a pipeline framework to maintain high flexibility  ...  To conduct knowledge-grounded dialogues with patients, we first construct a Chinese Medical Multi-Modal Knowledge Graph (CM3KG) and collect a large-scale Chinese Medical CQA (CMCQA) dataset.  ...  Conclusion In this paper, we introduce a conversational medical QA system, LingYi, which integrates multi-modal knowledge graphs CM3KG, and medical conversation dataset CMCQA.  ... 
arXiv:2204.09220v1 fatcat:t7ai6qosmzac7nbehowfmy4fg4

Applications of Multi-view Learning Approaches for Software Comprehension

Amir Saeidi, Jurriaan Hage, Ravi Khadka, Slinger Jansen
2019 The Art, Science, and Engineering of Programming  
high-level units and give component-level recommendations for refactoring of the system, as well as cross-view source code search.  ...  We employ state-of-the-art techniques from learning to 1) find a suitable similarity function for each view, and 2) compare different multi-view learning techniques to decompose a software system into  ...  Multi-view recommender systems can improve the accuracy of recommendations by transferring knowledge across the domains.  ... 
doi:10.22152/ fatcat:5bw467krprefzdhw7nvu4ib3wm

Multi-source knowledge fusion: a survey

Xiaojuan Zhao, Yan Jia, Aiping Li, Rong Jiang, Yichen Song
2020 World wide web (Bussum)  
, information fusion within KGs, multi-modal knowledge fusion and multi-source knowledge collaborative reasoning.  ...  On the one hand, the process of multi-source knowledge reasoning can detect conflicts and provide help for knowledge evaluation and verification; on the other hand, the new knowledge acquired by knowledge  ...  The environment system in the Reinforcement learning system is responsible for the dynamic interaction between knowledge graphs and Agent.  ... 
doi:10.1007/s11280-020-00811-0 fatcat:ef5j2sna6fai7k2455yihrrfuq

Modality Matches Modality: Pretraining Modality-Disentangled Item Representations for Recommendation

Tengyue Han, Pengfei Wang, Shaozhang Niu, Chenliang Li
2022 Proceedings of the ACM Web Conference 2022  
CCS CONCEPTS • Information systemsRecommender systems.  ...  To the best of our knowledge, this is the first pretraining framework to learn modalitydisentangled representations in recommendation scenarios.  ...  multi-modal approach, which builds user-item bipartite graph for each modal, then uses GCN to train each bipartite graph.  ... 
doi:10.1145/3485447.3512079 fatcat:aburalirkvam3mcyog3cidyoqm

Why Do We Click: Visual Impression-aware News Recommendation [article]

Jiahao Xun, Shengyu Zhang, Zhou Zhao, Jieming Zhu, Qi Zhang, Jingjie Li, Xiuqiang He, Xiaofei He, Tat-Seng Chua, Fei Wu
2021 arXiv   pre-print
To accurately capture users' interests, we propose to model multi-modal features, in addition to the news titles that are widely used in existing works, for news recommendation.  ...  for the content-based recommenders.  ...  DKN leverages entity embeddings from knowledge graphs as external knowledge for news recommendation. • NPA [31] .  ... 
arXiv:2109.12651v1 fatcat:pcjk6p7c4rbbrgc2hovl6zyfku

Hyper-node Relational Graph Attention Network for Multi-modal Knowledge Graph Completion

Shuang Liang, Anjie Zhu, Jiasheng Zhang, Jie Shao
2022 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
In this work, we propose a novel network to incorporate different modal information with graph structure information for more precise representation of multi-modal knowledge graph, termed as hyper-node  ...  In HRGAT, we use low-rank multi-modal fusion to model the intra-modality and inter-modality dynamics, which transforms the original knowledge graph to a hyper-node graph.  ...  [36] propose a multi-modal graph atention network to optimize the recommendation system with multi-modal knowledge graph. Qian et al.  ... 
doi:10.1145/3545573 fatcat:i4eviv6q4nd5xi2dkmysvxnqhy

Graph Neural Networks in Recommender Systems: A Survey

Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui
2022 ACM Computing Surveys  
Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority  ...  in graph representation learning.  ...  MKGAT [134] uniies the user nodes and multi-modal knowledge graph into one graph and employs a relation-aware graph attention network to propagate information.  ... 
doi:10.1145/3535101 fatcat:hgv2tbx3k5hzbnkupwsysqwjmy

Multi-modal Deep Analysis for Multimedia

Wenwu Zhu, Xin Wang, Hongzhi Li
2019 IEEE transactions on circuits and systems for video technology (Print)  
answering, multi-modal video summarization, multi-modal visual pattern mining and multi-modal recommendation.  ...  On knowledge-guided fusion, we discuss the approaches for fusing knowledge with data and four exemplar applications that require various kinds of domain knowledge, including multi-modal visual question  ...  ACKNOWLEDGMENT We thank Guohao Li, Shengze Yu and Yitian Yuan for providing relevant materials and valuable opinions. This work will never be accomplished without their useful suggestions.  ... 
doi:10.1109/tcsvt.2019.2940647 fatcat:l4tchrkgrnaeradvc4nhfan2w4
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