Content Visibility Personalization in Video Recommender Systems
Nalin Chakoo, Rahul Gupta, Jayaprada Hiremath · 2008
Current recommender systems based on filtering techniques implement a rather limited model for video content visibility. Most of these systems fall short to provide visual precursor to the user and concentrate only on making more accurate predictions; however, a few of them that focus their attention to the aspect of multimedia (video) item visibility do so in a limited scope. In this paper, we address this problem and propose to augment the existing recommender systems with a dynamic user-based scheme to provide users with superior, high-quality recommendation formulation and customized visibility of the recommended item. The domain of content visibility is dynamically crafted using the existing recommender system algorithm. performance of these systems. For example, viewing users' pattern and provision of solitary content recommendation does not address the problem of false positive recommendations. The recommender systems do not exploit the fact that users tend to like particular segments of the viewed content more than the rest. The extraction of these segments and their information can further enrich user experience, improve quality viewing and advance recommender systems. To address this belief, we put forward in this research paper work a novel Dynamic User Based Scheme, which facilitates the formation and maintenance of existing social networking architecture, and capitalizes its social trend for improved content recommendation, and sharing. Our work further helps in providing personalization and building a sophisticated recommender systems that track and recognize users' segment preferences in the viewed content.