Online stores and Web portals bring information about a myriad of items such as books, CDs, restaurants or movies at the user's fingertips. Although, the Web reduces the barrier to the information, the user is overwhelmed by the number of available items. Therefore, recommender systems aim to guide the user to  relevant items. Current recommender systems store user ratings on the server side. This way the scope of the recommendations is limited to this server only. In addition, the user entrusts the operator of the server with valuable information about his preferences.
In this paper we introduce the private, personal movie recommender OMORE, which learns the user model based on the user's movie ratings. To preserve privacy, OMORE is implemented as Firefox add-on which stores the user ratings and the learned user model locally at the client side. Although OMORE uses the features from the movie pages on the IMDb site, it is not restricted to IMDb only. The current implementation covers movie pages from Amazon.com, Blockbuster, Netflix and Rotten Tomatoes.

Download OMORE

MO, the movie ontology, is located at movieontology.org.

LiMo is a database providing cross-references among movies on the Web. The cross-references are on one hand generated automatically with similarity relation and on the other hand with the participatory effort of users.