The Linked Open Data (LOD) initiative has been quite successful in terms of publishing and interlinking data on the Web. On top of the huge amount of interconnected data, measuring relatedness between resources and identifying their relatedness could be used for various applications such as LOD-enabled recommender systems. In this paper, we propose various distance measures, on top of the basic concept of Linked Data Semantic Distance (LDSD), for calculating Linked Data semantic distance between resources that can be used in a LOD-enabled recommender system. We evaluated the distance measures in the context of a recommender system that provides the top-N recommendations with baseline methods such as LDSD. Results show that the performance is significantly improved by our proposed distance measures incorporating normalizations that use both of the resources and global appearances of paths in a graph.
With the popularity of Knowledge Graphs (KGs) in recent years, there have been many studies that leverage the abundant background knowledge available in KGs for the task of item recommendations. However, little attention has been paid to the incompleteness of KGs when leveraging knowledge from them. In addition, previous studies have mainly focused on exploiting knowledge from a KG for item recommendations, and it is unclear whether we can exploit the knowledge in the other way, i.e, whether user-item interaction histories can be used for improving the performance of completing the KG with regard to the domain of items. In this paper, we investigate the effect of knowledge transfer between two tasks: (1) item recommendations, and (2) KG completion, via a co-factorization model (CoFM) which can be seen as a transfer learning model. We evaluate CoFM by comparing it to three competitive baseline methods for each task. Results indicate that considering the incompleteness of a KG outperforms a state-of-the-art factorization method leveraging existing knowledge from the KG, and performs better than other baselines. In addition, the results show that exploiting user-item interaction histories also improves the performance of completing the KG with regard to the domain of items, which has not been investigated before.1 The prefix dbr denotes http://dbpedia.org/resource/.
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.In this paper, 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 bestperforming methods in each dimension. Results show that our strategies outperform two baseline strategies significantly in the context of link recommendations on Twitter.
With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain.
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