Construction of Internet TV Industry Ecosystem Based on Data Mining Technology

Abstract

While the Internet provides people with convenience, it also comes with security concerns. Users can more easily form groups, distort facts, and contribute to some sensitive topics on the internet. As a result, identifying and analyzing users’ online behaviors are critical. This paper creates a new Internet TV industry ecosystem using DM (data mining) technology. The recommendation system model is established based on data from users’ on-demand viewing behavior across the entire network, and the functions of various system modules and their coordination ability are described in detail. The evolution of users’ online time is examined, providing data to support and explain the prediction analysis of users’ click behavior and the analysis of users’ search intent. The type of web page that the user clicks on can reveal the user’s behavior tendency.

Interest-Based Content Clustering for Enhancing Searching and Recommendations on Smart TV

“…The statistics show that almost 94.2 million people are using smart TV in the United States (https://www.statista .com/statistics/718737/number-of-smart-tv-users-in-the-us/ ) only. Smart TV is a platform based on the full support of Web2.0 features where a user can read and write the online content [2]. It supports and provides content from diverse data sources like online stored video, video on demand (VoD) services, video clips, social networking sites, and support for online streaming channel [3].…”