Topical Semantic Recommendations for Auteur Films


With the ubiquity of fast internet connections and the growing availability of Video-On-Demand (VOD) services powerful recommender systems are needed. Traditionally, movie recommender systems apply user-based collaborative filtering providing high quality recommendations if users maintain user profiles describing preferences and movie ratings. The shortcomings of Collaborative Filtering are that comprehensive user profiles are required and users tend to get recommendations very similar to the user profile ("filter bubble"). In addition, CF-based recommenders neither consider current trends nor the context. In order to overcome these weaknesses, we develop a system identifying interesting events in the stream of current news and deploying this information for computing recommendations. Our system gathers topics of interest from Twitter and RSS-Feeds, extracts relevant Named Entities, and uses semantic relations for recommending movies closely related to these topics. We explain the used algorithms and show that our system provides highly relevant recommendations.

 author = {Rakow, Christian and Lommatzsch, Andreas and Plumbaum, Till},
 title = {Topical Semantic Recommendations for Auteur Films},
 booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems},
 series = {RecSys '16},
 year = {2016},
 isbn = {978-1-4503-4035-9},
 location = {Boston, Massachusetts, USA},
 pages = {403--404},
 numpages = {2},
 url = {},
 doi = {10.1145/2959100.2959110},
 acmid = {2959110},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {movie recommendations, named entities, semantic graphs},
Demo Paper
RecSys '16, September 15-19, 2016, Boston, MA, USA