Personalized event-based news video retrieval with dynamic user-log

Personalization especially in the domain of information retrieval is essentially important, as users might pose the same query even when they are searching for different information. It is thus necessary to create a retrieval engine which takes into consideration the dynamic information needs of different users. This paper presents our personalized news video retrieval engine, which exploits the individual userpsilas previous browsing history to customize and enhance their future search results. Specifically, the system utilizes the news topic hierarchy, a hierarchical news topic structure derived from unsupervised clustering on the news video corpus and event entities from news video and online news articles. We then dynamically project userpsilas browsing history onto this topic hierarchy to provide the basis for re-ranking relevant news videos. This system is tested on one month of TRECVID 2006 dataset consisting of 80 hours news video and found to return results in a more intuitive and personalized manner.