Guest editor’s introduction: special issue on analyzing and mining social networks for decision support and recommender systems

Mining and analyzing social networks is now becoming a very popular research area not only for data mining and web mining but also social network analysis. Data mining is a technique that has the ability to process and analyze large amounts of data and by this to discover valuable information from the data. In recent years, due to the booming of social communications and social network-based web services, data mining has become a very important and powerful technique to process and analyze such large amounts of data. Recently, many researchers are focusing on developing new data mining techniques and algorithms, or devoting to improve traditional mining techniques for social network analysis. One key test of this discovered data is how it can be used in real-world decision-making situations. Social data are the aggregations of the communications, interactions and experiences of people, and it is useful to leverage this type of data for decision-making. Thus, is an important time to balance the research focus and real world applications, such as decision support and recommender systems. In this special issue, we invited authors to submit extended versions of their papers, which were deemed top-quality papers from MISNC 2015 (The 2nd Multidisciplinary International Social Networks Conference, Matsuyama, Japan), MSNDS 2015 (The 6th International Workshop on Mining and Analyzing Social Networks for Decision Support, Paris, France) and ASE SocialInformatics 2015 (Kaohsiung, Taiwan). In addition to the invited papers, we also called for public submissions. Each submission was reviewed by at least two experts and revised according to reviewers’ comments to ensure the quality of the papers. The first paper is entitled “Impersonate human decision making process: an interactive context-aware recommender system” and was authored by Chen-Shu Wang, Shiang-Lin Lin and Heng-Li Yang. The authors alter traditional context-aware-recommender systems (CAR) to interactive CAR (iCAR) in order to improve the recommendation accuracy. A car rental J Intell Inf Syst (2016) 47:193–194 DOI 10.1007/s10844-016-0425-4