Combining Vector Space Model and Category Hierarchy Model for TV Content Similarity Measure

In this paper, we propose a new approach for TV content similarity measure, which combines both vector space model and category hierarchy model. The hybrid measure proposed here makes the most of TV metadata information and takes advantage of the two similarity measurements. It measures TV content similarity from the semantic level other than the physical level. Furthermore, we propose an adaptive strategy for setting the combination parameters. The experimental results showed that using the combining approach proposed here is superior to using either similarity measure alone for example-based retrieval of TV content.