Analysis of Media Content Recommendation in the New Media Era Considering Scenario Clustering Algorithm

Abstract

With the continuous progress of social economy, new media and micro media are constantly emerging in multiple ways, and the methods to access these media contents have become diversified as well. However, it should be noted that diverse types of media content in the era of big data also require excessive time spent in selecting the effective content. In response to these demands and defects, a scenario clustering algorithm is introduced in this paper, in which the media content recommendation is taken as the breakthrough point to build a clustering model to express the effective distribution of events by analyzing the network structure and media content distribution model through the analysis of the network structure and the distribution of the media content to represent the effective distribution of events and carry out the comparison of cross-content events, so as to achieve the effective clustering and analysis of media content. The results of the simulation experiment indicate that the scenario clustering algorithm proposed in this paper is effective and can support the analysis of media content recommendation in multiple dimensions, with the purpose to provide high-quality media services to users.