User modeling for individual users on the Social Web plays an important role and is a fundamental step for personalization as well as recommendations. Recent studies have proposed different user modeling strategies considering various dimensions such as temporal dynamics and semantics of user interests. Although previous work proposed different user modeling strategies considering the temporal dynamics of user interests, there is a lack of comparative studies on those methods and therefore the comparative performance over each other is unknown. In terms of semantics of user interests, background knowledge from DBpedia has been explored to enrich user interest profiles so as to reveal more information about users. However, it is still unclear to what extent different types of information from DBpedia contribute to the enrichment of user interest profiles. Here we propose user modeling strategies which use Concept Frequency - Inverse Document Frequency (CF-IDF) as a weighting scheme and incorporate either or both of the dynamics and semantics of user interests. To this end, we first provide a comparative study on different user modeling strategies considering the dynamics of user interests in previous literature to present their comparative performance. In addition, we investigate different types of information (i.e., categories, classes and connected entities via various properties) for entities from DBpedia and the combination of them for extending user interest profiles. Finally, we build our user modeling strategies incorporating either or both of the best-performing methods in each dimension. Results show that our strategies outperform two baseline strategies significantly in the context of link recommendations on Twitter.
Guangyuan Piao is a PhD student at the Insight Centre for Data Analytics at the National University of Ireland. He is working in the Unit for Social Semantics under the supervision of Dr. John G. Breslin, where his research focuses on user modeling and personalization on the Social Web such as Twitter. He worked in the automotive industry for over two years in South Korea before joining Insight Centre for Data Analytics. He obtained his master’s degree at Yonsei University, South Korea, and bachelor’s degree at Jilin University, China, respectively. His main research interests include user modeling, recommender systems, and semantic web.