So far it is unclear how different factors of a scientific publication recommender system based on users' tweets have an influence on the recommendation performance. We examine three different factors, namely profiling method, temporal decay, and richness of content. Regarding profiling, we compare CF-IDF that replaces terms in TF-IDF by semantic concepts, HCF-IDF as novel hierarchical variant of CF-IDF, and topic modeling. As temporal decay functions, we apply sliding window and exponential decay. In terms of the richness of content, we compare recommendations using both full-texts and titles of publications and using only titles. Overall, the three factors make twelve recommendation strategies. We have conducted an online experiment with 123 participants and compared the strategies in a within-group design. The best recommendations are achieved by the strategy combining CF-IDF, sliding window, and with full-texts. However, the strategies using the novel HCF-IDF profiling method achieve similar results with just using the titles of the publications. Therefore, HCF-IDF can make recommendations when only short and sparse data is available.
Time-aware user profiles are constructed based on the assumption that the degree of user interests declines as time passes. The decline of user interests is modeled by a decay function. In the past, the decay functions sliding window @cite_14 and exponential decay @cite_12 @cite_25 have been employed for user profiling. However, they have not been compared so far like we do in this work.
@cite_14 @cite_25 @cite_12
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User profiling techniques have mostly focused on retrieving and representing a user's knowledge, context and interests in order to provide recommendations, personalise search, and build user-adaptive systems. However, building a user profile on a single social network limits the quality and completeness of the profile, especially when interoperability of the profile is key and its reuse on different sites is necessary for providing other types of personalisation. Indeed recent studies have shown that users on the Social Web often use different social networking sites for diverse, and sometimes non-overlapping, purposes and interests. In this paper, we describe our methodology for the automatic creation and aggregation of interoperable and multi-domain user profiles of interests using semantic technologies. Moreover, we propose a user study on different user profiling techniques for social networking websites in general, and for Twitter and Facebook in particular. In this regard, based on the results of our user evaluation, we investigate (i) the accuracy of different methodologies for profiling, (ii) the effect of time decay functions on ranking user interests, and (iii) the benefits of merging different user models using semantic technologies. Type Ia supernova light curves are characterized by a rapid rise from zero luminosity to a peak value, followed by a slower quasi-exponential decline. The rise and peak last for a few days, while the decline persists for many months. It is widely believed that the decline is powered by the radioactive decay chain 56Ni → 56Co → 56Fe, but the rates of decline in luminosity do not exactly match the decay rates of Ni and Co. In 1976, Rust, Leventhal, and McCall [19] presented evidence that the declining part of the light curve is well modelled by a linear combination of two exponentials whose decay rates were proportional to, but not exactly equal to, the decay rates for Ni and Co. The proposed reason for the lack of agreement between the rates was that the radioactive decays take place in the interior of a white dwarf star, at densities much higher than any encountered in a terrestrial environment, and that these higher densities accelerate the two decays by the same factor. This paper revisits this model, demonstrating that a variant of it provides excellent fits to observed luminosity data from 6 supernovae. We present an analysis of Club Nexus, an online community at Stanford University. Through the Nexus site we were able to study a reflection of the real world community structure within the student body. We observed and measured social network phenomena such as the small world effect, clustering, and the strength of weak ties. Using the rich profile data provided by the users we were able to deduce the attributes contributing to the formation of friendships, and to determine how the similarity of users decays as the distance between them in the network increases. In addition, we found correlations between users' personalities and their other attributes, as well as interesting correspondences between how users perceive themselves and how they are perceived by others. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text.