Book chapter
Co-author Recommender System
Modern bibliographic databases contain significant amount of information on publication activities of research communities. Researchers regularly encounter challenging task of selecting a co-author for joint research publication or searching for authors, whose papers are worth reading. We propose a new recommender system for finding possible collaborator with respect to research interests. The recommendation problem is formulated as a link prediction within the co-authorship network. The network is derived from the bibliographic database and enriched by the information on research papers obtained from Scopus and other
publication ranking systems.
In book
In this paper we show that for a given co-authorship network we could construct a recommender system for searching collaborators with similar research interests defined via keywords and topic modelling. We suggest new link embedding method and evaluate our model on National Research University Higher School of Economics (NRU HSE) co-authorship network.
We present a new recommender system developed for the Russian interactive radio network FMhost. To the best of our knowledge, it is the first model and associated case study for recommending radio stations hosted by real DJs rather than automatically built streamed playlists. To address such problems as cold start, gray sheep, boosting of rankings, preference and repertoire dynamics, and absence of explicit feedback, the underlying model combines a collaborative user-based approach with personalized information from tags of listened tracks in order to match user and radio station profiles. This is made possible with adaptive tag-aware profiling that follows an online learning strategy based on user history. We compare the proposed algorithms with singular value decomposition (SVD) in terms of precision, recall, and normalized discounted cumulative gain (NDCG) measures; experiments show that in our case the fusion-based approach demonstrates the best results. In addition, we give a theoretical analysis of some useful properties of fusion-based linear combination methods in terms of graded ordered sets.
In this study, we investigated how scientific collaboration represented by co-authorship is related to citation indicators of a scientist. We use co-authorship network to explore the structure of scientific collaboration. For network construction, the profiles of scientists from various countries and scientific fields in Google Scholar were used. We ran the count data regression model for a sample of more than 30 thousand authors with the first citation after 2007 to analyze the correlation between co-authorship network parameters of scientists and their citation characteristics. We identify that there is a positive correlation between citation of scientist and number of his co-authors, between citation and the author’s closeness centrality, and between scholar’s citation and the average citation of his co-authors. Also, we reveal that h-index and i10-index are correlated significantly with the number of co-authors and average citation of co-authors. Based on these results, we can conclude that scientists who maintain more contacts and more active than others have better bibliometric indicators on an average.
We present a new recommender system developed for the Russian interactive radio network FMhost. To the best of our knowledge, it is the first model and associated case study for recommending radio stations hosted by real DJs rather than automatically built streamed playlists. To address such problems as cold start, gray sheep, boosting of rankings, preference and repertoire dynamics, and absence of explicit feedback, the underlying model combines a collaborative user-based approach with personalized information from tags of listened tracks in order to match user and radio station profiles. This is made possible with adaptive tag-aware profiling that follows an online learning strategy based on user history. We compare the proposed algorithms with singular value decomposition (SVD) in terms of precision, recall, and normalized discounted cumulative gain (NDCG) measures; experiments show that in our case the fusion-based approach demonstrates the best results. In addition, we give a theoretical analysis of some useful properties of fusion-based linear combination methods in terms of graded ordered sets.
The evaluation of research performance increasingly relies on quantitative indicators determined by national science policies. We focus on two dimensions of research performance—productivity and excellence—as defined in the evaluation methodology of the Slovenian Research Agency. Our analysis focuses on the effects of two science policy factors—co-authorship collaboration and researcher funding—on the productivity and excellence of Slovenian researchers at the level of research disciplines. A multilevel analysis using a hierarchical linear model with regression analysis was applied to the data with several nested levels. As many variables have a semi-continuous distribution, a statistical model was used to address them. The results show a very strong positive effect of international co-authorship collaboration on productivity and excellence, while fragmentation of funding shows a negative impact only on excellence. We also include interviews with excellent Slovenian researchers regarding their views on scientific excellence and quantitative indicators.
We present a new recommender system developed for the Russian interactive radio network FMhost based on a previously proposed model. The underlying model combines a collaborative user-based approach with information from tags of listened tracks in order to match user and radio station profiles. It follows an adaptive online learning strategy based on the user history. We compare the proposed algorithms and an industry standard technique based on singular value decomposition (SVD) in terms of precision, recall, and NDCG measures; experiments show that in our case the fusion-based approach shows the best results.