VizCommender: Computing Text-Based Similarity in Visualization Repositories for Content-Based Recommendations

Oppermann, Michael; Kincaid, Robert; Munzner, Tamara

doi:10.1109/tvcg.2020.3030387

Cited by 35 publications

(28 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, the mainstream recommendation algorithms in the recommendation system are divided into content-based recommendation algorithm [28], collaborative filtering recommendation algorithms [29], and hybrid recommendation algorithm. Among them, the most widely used recommendation algorithm is based on collaborative filtering and content-based.…”

Section: Multilayer Collaborative Filtering National Music Recommendation Modelmentioning

confidence: 99%

“…We believe that none of the existing research methods are suitable for jazz. On the one hand, Jazz may not be prevailing in our daily life, we have an insufficient example to train the model that has great experience; on the other hand, the 20000 Jazz [28] collaborative filtering recommendation [29] hybird recommendation [30] The number of Recommendation songs music is not distinctive to represent the Jazz that lead the Jazz lover unwilling to accept them. In addition, as we can see from Figure 10, Jazz's F1 has an improvement, which indicated that Jazz has a better recall.…”

Section: National Music Recommendation Model Testmentioning

confidence: 99%

See 1 more Smart Citation

Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching

Deng

2021

Security and Communication Networks

View full text Add to dashboard Cite

Personalized music recommendations can accurately push the music of interest from a massive song library based on user information when the user’s listening needs are blurred. To this end, this paper proposes a method of national music recommendation based on ontology modeling and context awareness to explore the use of music resources to portray user preferences better. First, the expectation-maximization algorithm is used to cluster users and ethnic music scores, and similar users and music are divided into clusters. The similarity of objects in the same cluster is higher, and the similarity of objects in different clusters is lower. Second, we designed a multilayer collaborative filtering ethnic music recommendation model based on ontology modeling and tensor decomposition. This model uses ontology to construct a user knowledge model and integrates similarity measures in multiple situations. The actual case test and user feedback analysis show that the designed personalized national music model has good application and promotion effects.

show abstract

Section: Multilayer Collaborative Filtering National Music Recommendation Modelmentioning

confidence: 99%

Section: National Music Recommendation Model Testmentioning

confidence: 99%

Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching

Deng

2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…'), and Temporal Connectives (e.g., 'before' and 'after'). For quantitative data, various aggregation functions (e.g., count, average, and sum) can be performed, binning and grouping operations are also commonly used [10], [39], [42], [52], [64], [85], [121], [147], [158], [179], [207], [210], [227], [280], [290]. For example, DeepEye [147] includes various aggregation functions in the visualization language.…”

Section: Transformation For Data Insightsmentioning

confidence: 99%

Towards Natural Language Interfaces for Data Visualization: A Survey

Shen,

Luo

et al. 2021

Preprint

View full text Add to dashboard Cite

Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary input modality to direct manipulation for visual analytics can provide an engaging user experience. It enables users to focus on their tasks rather than worrying about operating the interface to visualization tools. In the past two decades, leveraging advanced natural language processing technologies, numerous V-NLI systems have been developed both within academic research and commercial software, especially in recent years. In this article, we conduct a comprehensive review of the existing V-NLIs. In order to classify each paper, we develop categorical dimensions based on a classic information visualization pipeline with the extension of a V-NLI layer. The following seven stages are used: query understanding, data transformation, visual mapping, view transformation, human interaction, context management, and presentation. Finally, we also shed light on several promising directions for future work in the community.

show abstract

“…Therefore, new rule sets would likely be required to effectively recommend visualizations for each group. As user study has been a prevailing evaluation approach in visualization [13,15,17,23,27,32,34,35], many systems require experimenting with human users to validate the manually defined rules. In comparison, our work learns a visualization recommendation model M directly from a large corpus of training data, in a fully automatic data-driven fashion.…”

Section: Related Workmentioning

confidence: 99%

Learning to Recommend Visualizations from Data

Qian

Rossi

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

Visualization recommendation is important for exploratory analysis and making sense of the data quickly by automatically recommending relevant visualizations to the user. In this work, we propose the first end-to-end ML-based visualization recommendation system that leverages a large corpus of datasets and their relevant visualizations to learn a visualization recommendation model automatically. Then, given a new unseen dataset from an arbitrary user, the model automatically generates visualizations for that new dataset, derives scores for the visualizations, and outputs a list of recommended visualizations to the user ordered by effectiveness. We also describe an evaluation framework to quantitatively evaluate visualization recommendation models learned from a large corpus of visualizations and datasets. Through quantitative experiments, a user study, and qualitative analysis, we show that our end-to-end ML-based system recommends more effective and useful visualizations compared to existing state-of-the-art rule-based systems. CCS CONCEPTS• Information systems → Recommender systems; • Humancentered computing → Visualization systems and tools.

show abstract

VizCommender: Computing Text-Based Similarity in Visualization Repositories for Content-Based Recommendations

Cited by 35 publications

References 64 publications

Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching

Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching

Towards Natural Language Interfaces for Data Visualization: A Survey

Learning to Recommend Visualizations from Data

Contact Info

Product

Resources

About