Abstract

Recommender systems help users to discover relevant items. Traditionally, recommender systems rely on both detailed knowledge of the domain and an extensive user profile. However, small numbers of users, privacy concerns, or a very specific domain limit access or availability to this information. In this work, we present an approach for recommending items based on events relevant to the target group of our system. We exemplify the approach with the aid of a Video-On-Demand platform specialized in independent and art-house movies. Our recommender analyzes domain-specific blogs and news. It extracts current events that can be used for triggering topical recommendations. We show that our approach successfully identifies relevant events and provides highly relevant results without requiring detailed user profiles.

@inproceedings{DoerschLommatzsch:EventDrivenNewsRecommendation,
author = {Tobias D"{o}rsch and Andreas Lommatzsch},
title = {Topic Tracking in News Streams using Latent Factor Models},
booktitle = {Proc. of the LWDA conference, Track Knowledge Management (FG-WM)},
year = {2016},
series = {LWDA '16},
pages = {186-193},
isbn = {urn:nbn:de:0074-1670-3},
location = {Potsdam, Germany},
publisher = {CEUR Workshop Proceedings},
url = {http://ceur-ws.org/Vol-1670/paper-45.pdf},
}