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Recommendation has become one of the most important components of onlineservices for improving sale records, however visualization work for onlinerecommendation is still very limited. This paper presents an interactiverecommendation approach with the following two components. First, ratingrecords are the most widely used data for online recommendation, but they areoften processed in high-dimensional spaces that can not be easily understood orinteracted with. We propose a Latent Semantic Model (LSM) that captures thestatistical features of semantic concepts on 2D domains and abstracts userpreferences for personal recommendation. Second, we propose an interactiverecommendation approach through a storytelling mechanism for promoting thecommunication between the user and the recommendation system. Our approachemphasizes interactivity, explicit user input, and semantic information convey;thus it can be used by general users without any knowledge of recommendation orvisualization algorithms. We validate our model with data statistics anddemonstrate our approach with case studies from the MovieLens100K dataset. Ourapproaches of latent semantic analysis and interactive recommendation can alsobe extended to other network-based visualization applications, includingvarious online recommendation systems.

Interactive Movie Recommendation Through Latent Semantic Analysis and Storytelling