Interactive Movie Recommendation Through Latent Semantic Analysis and Storytelling
This addresses the need for more user-friendly and interpretable recommendation systems for general users, though it appears incremental as it builds on existing latent semantic and interactive methods.
The paper tackles the problem of limited visualization in online recommendation systems by proposing an interactive approach using a Latent Semantic Model (LSM) to capture semantic concepts in 2D for personalization and a storytelling mechanism for user engagement, validated with the MovieLens100K dataset.
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.