Recommendation 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.
Several interactive or automatic storytelling approaches have been developed for applications ranging from general visualization process @cite_3 @cite_7 to specific domains. For example, Wohlfart and Hauser @cite_23 used storytelling as a guided and interactive visualization presentation approach for medical visualization. @cite_28 detected geo-temporal patterns and integrates story narration to increase analytic sense-making cohesion in GeoTime. @cite_39 generated automatic animations with narrative structures extracted from event graphs for time-varying scientific visualization. @cite_17 presented a graph-driven approach for automatically identifying effective sequences in a set of visualizations to be presented linearly. @cite_10 presented a storytelling process with steps involved in finding insights, turning insights into a narrative, and communicating to an audience. Satyanarayan and Heer @cite_18 developed a model of storytelling abstractions and instantiate the model in Ellipsis with a graphical interface for story authoring. presented a narrative visualization system that presents literature review as interactive slides with three levels of narrative structures @cite_31 . @cite_12 generated textual annotations with a temporal layout and comic strip-style data snapshots for visualizing multidimensional and time-varying data.
@cite_18 @cite_7 @cite_28 @cite_3 @cite_39 @cite_23 @cite_31 @cite_10 @cite_12 @cite_17
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This paper presents a digital storytelling approach that generates automatic animations for time-varying data visualization. Our approach simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. Specifically, we analyze information related to a given event and abstract it as an event graph, which represents data features as nodes and event relationships as links. This graph embeds a tree-like hierarchical structure which encodes data features at different scales. Next, narrative structures are built by exploring starting nodes and suitable search strategies in this graph. Different stages of narrative structures are considered in our automatic rendering parameter decision process to generate animations as digital stories. We integrate this animation generation approach into an interactive exploration process of time-varying data, so that more comprehensive information can be provided in a timely fashion. We demonstrate with a storm surge application that our approach allows semantic visualization of time-varying data and easy animation generation for users without special knowledge about the underlying visualization techniques. In this paper we present a novel approach to volume visualization for presentation purposes that improves both the comprehensibility and credibility of the intended visualization message. Therefore, we combine selected aspects from storytelling as well as from interactive volume visualization to create a guided but at the same time interactive visualization presentation approach. To ease the observer's access to a presented visualization result we not only communicate the result itself, but also deliver its creational process in the form of an annotated visualization animation, which we call a visualization story. Additionally, we enable variable means of interactivity during story playback. The story observers may just watch the presentation passively, but they are also allowed to reinvestigate the visualization independently from story guidance, offering the ability to verify, confirm, or even disapprove the presented visualization message. For demonstration purposes, we developed a prototype application that provides tools to author, edit, and watch visualization stories. We demonstrate the potential of our approach on the basis of medical visualization examples. Data visualization is now a popular medium for journalistic storytelling. However, current visualization tools either lack support for storytelling or require significant technical expertise. Informed by interviews with journalists, we introduce a model of storytelling abstractions that includes state-based scene structure, dynamic annotations and decoupled coordination of multiple visualization components. We instantiate our model in Ellipsis: a system that combines a domain-specific language DSL for storytelling with a graphical interface for story authoring. User interactions are automatically translated into statements in the Ellipsis DSL. By enabling storytelling without programming, the Ellipsis interface lowers the threshold for authoring narrative visualizations. We evaluate Ellipsis through example applications and user studies with award-winning journalists. Study participants find Ellipsis to be a valuable prototyping tool that can empower journalists in the creation of interactive narratives. We propose a hierarchically structured reinforcement learning approach to address the challenges of planning for generating coherent multi-sentence stories for the visual storytelling task. Within our framework, the task of generating a story given a sequence of images is divided across a two-level hierarchical decoder. The high-level decoder constructs a plan by generating a semantic concept (i.e., topic) for each image in sequence. The low-level decoder generates a sentence for each image using a semantic compositional network, which effectively grounds the sentence generation conditioned on the topic. The two decoders are jointly trained end-to-end using reinforcement learning. We evaluate our model on the visual storytelling (VIST) dataset. Empirical results from both automatic and human evaluations demonstrate that the proposed hierarchically structured reinforced training achieves significantly better performance compared to a strong flat deep reinforcement learning baseline.