Personalized channel recommendation on live streaming platforms

Abstract

With unceasing technological advancements, an increasing number of viewers are watching channels through live streaming platforms, and live streaming technologies are developing rapidly. However, as thousands of channels are broadcasting on live streaming platforms, it is difficult for viewers to find their favorite channels. As a result, an accurate channel recommendation technique is required for the viewers. The current method of promoting live streaming channels recommends the most popular channels to viewers, but this ignores viewers' personal preferences. Therefore, we cluster viewers based on their personal preferences so that one cluster of viewers contains the viewers with similar favorite channels. In this way, the channels liked by viewers can be recommended to other viewers in the same group. In addition, our recommendation technique also considers viewers' loyalty towards a particular channel. In the experiment, a currently popular live streaming gaming platform, Twitch, is used for the analysis. The results confirm that our proposed recommendation technique is more accurate than the existing recommendation techniques.