The study of tourism destination images is of great significance in the tourism discipline.Tourism user-generated content(UGC),i.e.,the feedback on tourism websites,provides rich information for constructing a destina...The study of tourism destination images is of great significance in the tourism discipline.Tourism user-generated content(UGC),i.e.,the feedback on tourism websites,provides rich information for constructing a destination image.However,it is difficult for tourism researchers to obtain a relatively complete and intuitive destination image due to the unintuitive destination image display,the significant variance in departure time and data length,and the destination type in UGC.We propose TDIVis,a carefully designed visual analytics system,aimed at obtaining a relatively comprehensive destination image.Specifically,a keyword-based sentiment visualization method is proposed to associate the cognitive image with the emotional image,and by this method,both time evolution analysis and classification analysis are considered;a multi-attribute association double sequence visualization method is proposed to associate two different types of text sequences and provide a dynamic visual encoding interaction method for the multi-attribute characteristics of sequences.The effectiveness and usability of TDIVis are demonstrated through four cases and a user study.展开更多
Abundant tourism user-generated content(UGC)contains a wealth of cognitive and emotional in-formation,providing valuable data for building destination images that depict tourists’experiences and appraisal of the dest...Abundant tourism user-generated content(UGC)contains a wealth of cognitive and emotional in-formation,providing valuable data for building destination images that depict tourists’experiences and appraisal of the destinations during the tours.In particular,multiple destination images can assist tourism managers in exploring the commonalities and differences to investigate the elements of interest of tourists and improve the competitiveness of the destinations.However,existing methods usually focus on the image of a single destination,and they are not adequate to analyze and visualize UGC to extract valuable information and knowledge.Therefore,we discuss requirements with tourism experts and present MDIVis,a multi-level interactive visual analytics system that allows analysts to comprehend and analyze the cognitive themes and emotional experiences of multiple destination images for comparison.Specifically,we design a novel sentiment matrix view to summarize multiple destination images and improve two classic views to analyze the time-series pattern and compare the detailed information of images.Finally,we demonstrate the utility of MDIVis through three case studies with domain experts on real-world data,and the usability and effectiveness are confirmed through expert interviews.展开更多
User-generated content(UGC) such as blogs and twitters are exploding in modern Internet services. In such systems, recommender systems are needed to help people filter vast amount of UGC generated by other users. Howe...User-generated content(UGC) such as blogs and twitters are exploding in modern Internet services. In such systems, recommender systems are needed to help people filter vast amount of UGC generated by other users. However, traditional recommendation models do not use user authorship of items. In this paper, we show that with this additional information, we can significantly improve the performance of recommendations. A generative model that combines hierarchical topic modeling and matrix factorization is proposed. Empirical results show that our model outperforms other state-of-the-art models, and can provide interpretable topic structures for users and items. Furthermore, since user interests can be inferred from their productions, recommendations can be made for users that do not have any ratings to solve the cold-start problem.展开更多
基金Project supported by the Science&Technology Department of Sichuan Province,China(No.2018GZ0171)the Chengdu Science and Technology Bureau,China(No.2015-HM01-00484-SF)。
文摘The study of tourism destination images is of great significance in the tourism discipline.Tourism user-generated content(UGC),i.e.,the feedback on tourism websites,provides rich information for constructing a destination image.However,it is difficult for tourism researchers to obtain a relatively complete and intuitive destination image due to the unintuitive destination image display,the significant variance in departure time and data length,and the destination type in UGC.We propose TDIVis,a carefully designed visual analytics system,aimed at obtaining a relatively comprehensive destination image.Specifically,a keyword-based sentiment visualization method is proposed to associate the cognitive image with the emotional image,and by this method,both time evolution analysis and classification analysis are considered;a multi-attribute association double sequence visualization method is proposed to associate two different types of text sequences and provide a dynamic visual encoding interaction method for the multi-attribute characteristics of sequences.The effectiveness and usability of TDIVis are demonstrated through four cases and a user study.
基金This work was supported by the Chengdu Science and Tech-nology Bureau,China(Grant No.2019-YF05-02121-SN).
文摘Abundant tourism user-generated content(UGC)contains a wealth of cognitive and emotional in-formation,providing valuable data for building destination images that depict tourists’experiences and appraisal of the destinations during the tours.In particular,multiple destination images can assist tourism managers in exploring the commonalities and differences to investigate the elements of interest of tourists and improve the competitiveness of the destinations.However,existing methods usually focus on the image of a single destination,and they are not adequate to analyze and visualize UGC to extract valuable information and knowledge.Therefore,we discuss requirements with tourism experts and present MDIVis,a multi-level interactive visual analytics system that allows analysts to comprehend and analyze the cognitive themes and emotional experiences of multiple destination images for comparison.Specifically,we design a novel sentiment matrix view to summarize multiple destination images and improve two classic views to analyze the time-series pattern and compare the detailed information of images.Finally,we demonstrate the utility of MDIVis through three case studies with domain experts on real-world data,and the usability and effectiveness are confirmed through expert interviews.
基金Project supported by the Monitoring Statistics Project on Agricultural and Rural Resources,MOA,Chinathe Innovative Talents Project,MOA,Chinathe Science and Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences(No.CAAS-ASTIP-2015-AI I-02)
文摘User-generated content(UGC) such as blogs and twitters are exploding in modern Internet services. In such systems, recommender systems are needed to help people filter vast amount of UGC generated by other users. However, traditional recommendation models do not use user authorship of items. In this paper, we show that with this additional information, we can significantly improve the performance of recommendations. A generative model that combines hierarchical topic modeling and matrix factorization is proposed. Empirical results show that our model outperforms other state-of-the-art models, and can provide interpretable topic structures for users and items. Furthermore, since user interests can be inferred from their productions, recommendations can be made for users that do not have any ratings to solve the cold-start problem.
