HUNG CHAU's blog

Dario De Nart, Felice Ferrara, and Carlo Tasso ( UMAP-2013 ) http://link.springer.com/chapter/10.1007/978-3-642-38844-6_26 In the paper “Personalized Access to Scientific Publications: from Recommendation to Explanation”, Dario De Nart et al. present a Recommender and Explanation System (RES) that is able to recommend scientific publication and show explanation to users.  In order to do recommendation, Dario De Nart et al., firstly, use the The Dikpe Keyphrase extraction algorithm introduced by Pudota (2010) to extract keyphrases from papers. Each keyphrase has a weight called keyphraseness that reveals the several lexical and statistical indicators exploited in the extraction process. Higher is the keyphraseness, more relevant is the KP. For each paper in the collection, they represent it as a conceptual graph. Each node in the graph is a term broken down from a KP. Two nodes are connected when they appear in the same KP. For instance, the KP “document retrieval” is split int...

Suzan Verberne, Maya Sappelli, Wessel Kraaij ( UMAP-2013 ) http://sverberne.ruhosting.nl/papers/swell_umap_cameraready.pdf There are several methods that social information systems use to recommend people to their users. Among these methods, content-based people recommendation is most widely used in many different systems (e.g., Twitter). The systems collect information from users such as publications, blogs, microblogs, or posts to build their profiles in order to do recommendation later. How to build effective user profiles from texts is a difficult task. Most of the studies represent texts as a bag-of-word. Some other try to extract more meaningful terms or phrases. If a system has a good representation for user profile, it will be able to find more accurately similar users so that it can generate good people recommendation to each user. In this paper, Verberne et al. present a study that compares three popular methods of weighting terms for user profiling. The results of this ...