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Multimodal interactive network for Sequential Recommendation

Journal of Computer Science & Technology 2023年第4期38卷 911-926页

作者：韩滕跃王鹏飞牛少彰 Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing 100876China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and items.Recent work has shown the effectiveness of incorporating textual or visual information into sequential recommendation to alleviate the data sparse problem.The data sparse problem now is attracting a lot of attention in both industry and academic community.However,considering interactions among modalities on a sequential scenario is an interesting yet challenging task because of multimodal heterogeneity.In this paper,we introduce a novel recommendation approach of considering both textual and visual information,namely Multimodal interactive network(MIN).The advantage of MIN lies in designing a learning framework to leverage the interactions among modalities from both the item level and the sequence level for building an efficient system.Firstly,an item-wise interactive layer based on the encoder-decoder mechanism is utilized to model the item-level interactions among modalities to select the informative information.Secondly,a sequence interactive layer based on the attention strategy is designed to capture the sequence-level preference of each modality.MIN seamlessly incorporates interactions among modalities from both the item level and the sequence level for sequential recommendation.It is the first time that interactions in each modality have been explicitly discussed and utilized in sequential recommenders.Experimental results on four real-world datasets show that our approach can significantly outperform all the baselines in sequential recommendation task.

关键词： multi-modality interactive network sequential recommendation

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MINE:A Method of Multi-Interaction Heterogeneous Information network Embedding

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Computers, Materials & Continua 2020年第6期63卷 1343-1356页

作者： Dongjie Zhu Yundong Sun Xiaofang Li Haiwen Du Rongning Qu Pingping Yu Xuefeng Piao Russell Higgs Ning Cao School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China Department of Mathematics Harbin Institute of TechnologyWeihai264209China School of Astronautics Harbin Institute of TechnologyHarbin150001China School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050018China School of Mathematics and Statistics University College DublinDublinD04 V1W8Ireland School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China

Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.

关键词： network embedding network representation learning interactive network data mining

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基于新闻大数据的北极地区地缘关系研究

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Journal of Geographical Sciences 2022年第10期32卷 2036-2052页

作者：李萌袁文袁武牛方曲李汉青胡段牧 Institute of Geographic Sciences and Natural Resources Research CASBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science&Technology BITBeijing 100081China First Research Institute of the Ministry of Public Security of PRC Beijing 100048China

Global warming has caused the Arctic Ocean ice cover to shrink.This endangers the environment but has made traversing the Arctic channel possible.Therefore,the strategic position of the Arctic has been significantly improved.As a near-Arctic country,China has formulated relevant policies that will be directly impacted by changes in the international relations between the eight Arctic countries(regions).A comprehensive and real-time analysis of the various characteristics of the Arctic geographical relationship is required in China,which helps formulate political,economic,and diplomatic countermeasures.Massive global real-time open databases provide news data from major media in various countries.This makes it possible to monitor geographical relationships in real-time.This paper explores key elements of the social development of eight Arctic countries(regions)over 2013-2019 based on the GDELT database and the method of labeled latent Dirichlet allocation.This paper also constructs the national interaction network and identifies the evolution pattern for the relationships between Arctic countries(regions).The following conclusions are drawn.(1)Arctic news hotspot is now focusing on climate change/ice cap melting which is becoming the main driving factor for changes in geographical relationships in the Arctic.(2)There is a strong correlation between the number of news pieces about ice cap melting and the sea ice area.(3)With the melting of the ice caps,the social,economic,and military activities in the Arctic have been booming,and the competition for dominance is becoming increasingly fierce.In general,there is a pattern of domination by Russia and Canada.

关键词： Arctic geographical relationship spatiotemporal data mining topic model interactive network big data

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