For problems such as search and recommendation of video content to users it is quite important to have reliable algorithms for categorical classification. Text-based approaches to such problems are often insufficient due to a lack of textual information about documents. A content analysis-based approach may be more effective but it is very resource-intensive.
As the categorical classification problem is similar to adult video detection it is reasonable to try similar approaches. In current work we apply two different graph-based algorithms to expand text-based classification. The first algorithm, which has shown its efficiency for adult video detection, uses a bipartite users-documents graph and naive Bayes approach to calculate the probability that a document belongs to a particular category. The second algorithm is a modification of the link graph-based algorithm described in . We apply these algorithms to the following categorical markups: Games, Movies, Music, News, Movie Trailers, HowTo.