The 1st International Workshop on Human Aspects of Making Recommendations in Social Ubiquitous Networking Environments (HRSUNE) is held on June 16th, 2014, in Macau (China) in conjunction with the 21st International Conference on Web-Age Information Management (WAIM 2014).

With the hugely popular social-rich information environments (e.g. Netflix, Yelp, Facebook, Twitter, Google+) penetrating our daily life, people (and organizations) have become more powerless with the flooding information from which decisions must be made. Fortunately, Recommender systems are known to be capable of implicitly or explicitly observing users' online activities, learning their likes and dislikes and making personalized (or group-wise) suggestions accordingly. They have become a well-integrated part of a vast number of web/mobile applications available in the cloud and have been used in a wide variety of application areas such as (digital) entertainment (e.g. news articles, music, movies, books, restaurants, etc.), software engineering (for example, recommending replacement methods for adaptive codes; recommending reusable codes from the Internet etc), and e-learning contexts (gathering interactions during the learning process both in formal and informal learning scenarios through learning management systems, virtual learning communities and personal learning environments).

While the majority of earlier research efforts have been focused on the algorithmic understanding of making recommendations, more recent ones have aimed at understanding human and social factors of making suggestions and sharing resources (e.g., content items, people, software widgets, etc.) in existing social ubiquitous networks to answer questions such as, among many others:

  1. What types of resources (for example, news articles) are mostly likely to be shared and liked/disliked?
  2. Does human factors matter when rating a resource (and thus, are to be taken into account in the recommendation process) such as the users' mood and emotions or the social ubiquitous environment where the resources is consumed?
  3. What effects do the 'share'/'like'/'follow' buttons have on people's information-seeking behaviors; in other words, should traditional recommendation techniques integrate these non-numeric ratings in making suggestions? If so, how?
  4. What effects would reviews provided by other users have over the popularity/fall of a resource in a social network and does this effect depends on the context where the review has been made?

This workshop aims at bringing together researchers and practitioners to explore and share their research results on the human and social aspects of making recommendations in the emerging social and increasingly more and more ubiquitous networking environments.