Recommendation and visualization of similar movies using minimum spanning dendrograms

Vlachos, Michail; Svonava, Daniel

doi:10.1177/1473871612439644

Cited by 6 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The project results in this category were typically full-scale implementations of the Information Seeking Mantra (Shneiderman, 1996) serving undirected exploration purposes rather than focused research interests. The IMDb database 3 suited perfectly well in this constellation as it provides various easily understandable information with different types, and only few sophisticated visualizations (Vlachos and Svonava, 2013;Alam and Jianu, 2016;Auber et al, 2003) have been proposed to explore film history in the spirit of the Filmfinder (Ahlberg and Shneiderman, 1994). Related student projects in my course included developing visual designs to explore movie relationships, biographies of persons such as actors or directors, and film locations.…”

Section: The Fake Humanities Scholarmentioning

confidence: 99%

Teaching on the Intersection of Visualization and Digital Humanities

Jänicke

2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

View full text Add to dashboard Cite

Visualization as a means to generate hypotheses and to communicate insights on digitized cultural heritage data sets has become more and more important in the recent years. While many digital humanities researchers transform their data in order to be processed with ready-to-use tools, others engage in interdisciplinary collaborations with visualization scholars aiming to design novel solutions and interactive visual interfaces as occurring data features are more carefully mapped to visual attributes. Many of those collaborations suffer from loss of time at the beginning of the project as scholars with diverse research backgrounds require to understand each others' mindsets, ways of thinking and research interests. This paper reports on three years of teaching visualization design for digital humanities projects. It provides an overview of theoretical course contents, practical training, collaboration setups-involving computer science, digital humanities as well as humanities students who experienced typical collaboration obstacles-and remarkable project results.

show abstract

Section: The Fake Humanities Scholarmentioning

confidence: 99%

Teaching on the Intersection of Visualization and Digital Humanities

Jänicke

2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

View full text Add to dashboard Cite

show abstract

“…Crnovrsanin et al [5] proposed a taskbased and information-based network representation for users to interact and visualize a recommendation list. Vlachos et al [38] used bipartite graphs and minimum spanning trees to explore and visualize recommendation results of a movie-actor dataset.…”

Section: Visualization For Recommendationmentioning

confidence: 99%

Interactive Storytelling for Movie Recommendation through Latent Semantic Analysis

Wegba

et al. 2018

23rd International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

Figure 1: An example from our latent semantic model for interactive recommendation and abstraction of user preferences. Our approach identifies a 2D visualization domain, where the horizontal axis layouts recommendable movies on a latent dimension between two combined movie features that are selected based on the user's watch history, and the vertical axis uses recommendation degrees to move highly recommendable movies to the top. This example demonstrates the preference of a user on drama/documentary/biography movies (green zone toward the right) over comedy/music genres (orange zone toward the left). The movies selected to recommend are enlarged as blue circles, recommendable movies are shown as purple nodes, watched and liked movies as green nodes, and disliked movies as orange nodes. Two example movie posters, one liked movie "Casino" and one disliked movie "Airheads", are also provided to demonstrate the latent dimension. For illustration purpose, we also add the arrowed line at the bottom and several movie titles to confirm the movie distributions on the visualization domain. ABSTRACTRecommendation has become one of the most important components of online services for improving sale records, however visualization work for online recommendation is still very limited. This paper presents an interactive recommendation approach with the following two components. First, rating records are the most widely used data for online recommendation, but they are often processed in high-dimensional spaces that can not be easily understood or interacted with. We propose a Latent Semantic Model (LSM) that captures the statistical features of semantic concepts on 2D domains and abstracts user preferences for personal recommendation. Second, we propose an interactive recommendation approach through a storytelling mechanism for promoting the communication between the user and the recommendation system. Our approach emphasizes interactivity, explicit user input, and semantic information convey; thus it can be used by general users without any knowledge of recommendation or visualization algorithms. We validate our model with data statistics and demonstrate our approach with case studies from the MovieLens100K dataset. Our approaches of latent semantic analysis and interactive recommendation can also be extended to other network-based visualization applications, including various online recommendation systems.

show abstract

“…There has been some work to use the node-link graph to visualize movie data [18]. For example, Svonava and Vlachos proposed a scale not only applicable to the distance scale but also applicable to nondistance method, by which the relationships between the films were visualized [10]. The method of [18] proposed by Ahmed et al mainly focused on cooperation between the actors in the 4-ring structure, and the nodes are connected by parallel coordinates chart considering both the details and the overview.…”

Section: Visualization Of Movie Datamentioning

confidence: 99%

“…There has been some work on visualization of movie data which mainly focus on certain types of movie data, such as the actors/directors network with the characteristics of "small-world networks" [4][5][6][7][8] or the movie scores and other statistical movie data. These works only visualize one mode of actors [9] or movies [10] and analyse partnerships between actors or similarity between movies by movie awards or other movie box office indicators. There is no visualization work considering the three modes of movie, actors, and directors simultaneously, that is, presenting the network data of moviedirector-actor at the same time.…”

Section: Introductionmentioning

confidence: 99%

Visual Analysis of Multimodal Movie Network Data Based on the Double-Layered View

Tang¹,

Yu²,

Li³

et al. 2015

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Multimodal visualization of network data is a method considering various types of nodes and visualizing them based on their types, or modes. Compared to traditional network visualization of nodes of the same mode, the new method treats different modes of entities in corresponding ways and presents the relations between them more clearly. In this paper, we apply the new method to visualize movie network data, a typical multimodal graph data that contains nodes of different types and connections between them. We use an improved force-directed layout algorithm to present the movie persons as the foreground and a density map to present films as the background. By combining the foreground and background, the movie network data are presented in one picture properly. User interactions are provided including detailed pie charts visible/invisible, zooming, and panning. We apply our visualization method to the Chinese movie data from Douban website. In order to testify the effectiveness of our method, we design and perform the user study of which the statistics are analyzed.

show abstract

Recommendation and visualization of similar movies using minimum spanning dendrograms

Cited by 6 publications

References 34 publications

Teaching on the Intersection of Visualization and Digital Humanities

Teaching on the Intersection of Visualization and Digital Humanities

Interactive Storytelling for Movie Recommendation through Latent Semantic Analysis

Visual Analysis of Multimodal Movie Network Data Based on the Double-Layered View

Contact Info

Product

Resources

About