Interactive Storytelling for Movie Recommendation through Latent Semantic Analysis
Interactive Storytelling for Movie Recommendation through Latent Semantic Analysis
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DOI：
10.1145/3172944.3172979
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Kodzo Wegba;Aidong Lu;Yuemeng Li;Wencheng Wang
中科院分区：
文献类型：
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作者：
Kodzo Wegba;Aidong Lu;Yuemeng Li;Wencheng Wang
推荐是许多在线服务必不可少的，但目前的系统往往提供有限的交互和可视化机制，影响用户的满意度推荐。本文提出了一种交互式推荐方法，为公众没有任何知识的推荐或可视化算法。我们的方法强调交互性，明确的用户输入，语义信息传达与以下两个组成部分。首先，我们提出了一个潜在的语义模型，捕捉统计特征的语义概念的2D域和抽象的用户偏好的个人推荐，使高维谱空间的评分记录可以理解和直接交互。其次，我们提出了一个互动的推荐方法，通过一个讲故事的机制，促进用户和推荐系统之间的沟通。我们证明和评估我们的方法与真实的数据集。我们的方法也可以扩展到其他应用程序，包括各种在线推荐系统。
Recommendation is essential to many online services; however current systems often provide limited interaction and visualization mechanisms, affecting the user satisfaction of recommendation. This paper presents an interactive recommendation approach for the general public without any knowledge of recommendation or visualization algorithms. Our approach emphasizes interactivity, explicit user input, and semantic information convey with the following two components. First, we propose a Latent Semantic Model that captures the statistical features of semantic concepts on 2D domains and abstracts user preferences for personal recommendation, so that high-dimensional spectral space from the rating records can be understood and interacted with directly. Second, we propose an interactive recommendation approach through a storytelling mechanism for promoting the communication between the user and the recommendation system. We demonstrate and evaluate our approach with a real dataset. Our approach can also be extended to other applications including various online recommendation systems.