Exploiting Knowledge Structure for Proximity-aware Movie Retrieval Model

Sansung Kim, Keejun Han, Mun Yong Yi, Sinhee Cho, Seongchan Kim

Abstract: Current movie title retrieval models, such as IMDB, mainly focus on utilizing structured or semi-structured data. However, user queries for searching a movie title are often based on the movie plot, rather than its metadata. As a solution to this problem, our movie title retrieval model proposes a new way of elaborately utilizing associative relations between multiple key terms that exist in the movie plot, in order to improve search performance when users enter more than one keyword. More specifically, the proposed model exploits associative networks of key terms, called knowledge structures, derived from the movie plots. Using the search query terms entered by Amazon Mechanical Turk users as the golden standard, experiments were conducted to compare the proposed retrieval model with the extant state-of-the-art retrieval models. The experiment results show that the proposed retrieval model consistently outperforms the baseline models. The findings have practical implications for semantic search of movie titles particularly, and of online entertainment contents in general.