Stories Around You: Location-based Serendipitous Recommendation of News Articles.
Yonata Andrelo Asikin, Wolfgang Wörndl · 2014
Abstract. Existing studies in serendipitous recommendation mostly fo-cus on extending the metrics of desired goals such as accuracy, novelty and serendipity with respect to the user preferences. This work aims at serendipity by exploiting the prevailing location (spatial) contexts of the recommendation. For this purpose, we propose a novel spatial context model and a number of recommendation techniques based on the model. A user study on a real news dataset shows that our approach outper-forms the baseline distance-based approach and thereby improves the overall user satisfaction with the recommendation result in the absence of the user’s personal information. Key words: serendipity, location-based recommender systems 1