By Chirag Shah
Today’s complicated, information-intensive difficulties usually require humans to interact. generally those projects pass a long way past easily looking jointly; they comprise details look up, sharing, synthesis, and decision-making. additionally, all of them have an end-goal that's collectively invaluable to all events concerned. Such “collaborative details looking” (CIS) tasks usually final numerous classes and the individuals all percentage an goal to give a contribution and gain. now not unusually, those strategies are hugely interactive.
Shah specializes in separately well-understood notions: collaboration and data looking, with the target of bringing them jointly to teach the way it is a ordinary tendency for people to interact on complicated projects. the 1st a part of his e-book introduces the final notions of collaboration and data looking, in addition to similar strategies, terminology, and frameworks; and hence presents the reader with a complete remedy of the recommendations underlying CIS. the second one a part of the e-book info CIS as a standalone area. a sequence of frameworks, theories, and types are brought to supply a conceptual foundation for CIS. the ultimate half describes numerous structures and functions of CIS, in addition to their broader implications on different fields akin to computer-supported cooperative paintings (CSCW) and human-computer interplay (HCI).
With this primary accomplished evaluation of a thrilling new examine box, Shah provides to graduate scholars and researchers in academia and an encompassing description of the applied sciences concerned, state of the art effects, and open demanding situations in addition to study opportunities.
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Additional info for Collaborative Information Seeking: The Art and Science of Making the Whole Greater than the Sum of All
As we saw earlier, collaboration among users based on common information need and asynchronous communication flow results in collaborative filtering. In other words, collaborative filtering refers to a process in which a user benefits from other users’ past actions on the same/similar information seeking tasks. In practice, this concept is realized as recommender systems. com , movies recommendations by MovieLens , and news filtering by VERSIFI Technologies . Often the systems that are built for supporting collaborative filtering are promoted as CIS systems.
The analysis of social networks, on the other hand, has been a well-studied domain for nearly a century and is being adapted to the newly emerged online social networking sites. What is of interest here is the realization that some of the aspects of CIS, particularly communication, are analyzed extensively in social networking research. In addition to this, research in CIS can also benefit from the understanding developed 32 3 Collaborative Information Seeking (CIS) in Context from social network analysis about how and why people work with each other, the costs and benefits of such collaborations, and user behavior in these ties or networks.
Now, if we had a “smart” system, it will monitor this user’s behavior over time and use it in new information seeking processes (Fig. 3). The behavior refers to the kind of queries that the user submits, the results that he views, and the information that he saves. In other words, this smart system learns the user’s information seeking model and uses it to aid in future information seeking processes. An example is online movie renting services such as Blockbuster and Netflix. Based on the kind of movies a user has rented in the past, as well as the ratings that he assigned, the system recommends new movies to him.
Collaborative Information Seeking: The Art and Science of Making the Whole Greater than the Sum of All by Chirag Shah