Social Navigation

Abstract

In this chapter, we present one of the pioneering approaches in supporting users in navigating complex information spaces: social navigation support. Social navigation support is inspired by the natural tendencies of individuals to follow traces of each other in exploring the world, especially when dealing with uncertainties. In this chapter, we cover details of various approaches in implementing social navigation support in the information space, and we connect the concept to supporting theories. The first part of this chapter reviews related theories and introduces the design space of social navigation support through a series of sample applications. The second part of the chapter discusses the common challenges in design and implementation of social navigation support, demonstrates how these challenges have been addressed, and reviews the more recent direction of social navigation support. Furthermore, as social navigation support has been an inspirational approach for various other social information access approaches, we discuss how social navigation support can be integrated with those approaches. We conclude with a review of evaluation methods for social navigation support and remarks about its current state.

Social Search

“…In this section, we consider SERP augmentation: the different ways that the SERP can be decorated with additional information to assist the searcher. SERP augmentation is, in some aspects, similar to the link augmentation in social navigation reviewed in Chapter 5 of this book [71]. There has been a long history of research into how search results might be presented to users so that they can better understand their relevance.…”

“…Research on using navigation and annotation activity of past users to augment search results was performed mostly in parallel with the work on using query sessions, though it was originally motivated by the ideas of social navigation rather than social search. The social navigation research stream in early 2000, as reviewed in Chapter 5 of this book [71], aimed to guide users through an information space by augmenting navigation links with "social wisdom" extracted from the history of past navigation. The main impediment to using this approach in a search context is selecting a meaningful subset of users, such that their browsing data will be helpful in selecting query results.…”

“…In the final section of this chapter, we showcase two further projects that go beyond the usual borders of social search systems to connect social search technologies with other types of social information access. The ASSIST platform, which was discussed earlier, demonstrates how social search can be integrated with social navigation, another social information access technology reviewed in Chapter 5 of this book [71]. ASSIST shows how these technologies can collaborate by using community browsing data to improve search, but also by using social data collected during search to improve navigation.…”

Exploring the Design of History-Enriched Floor Interfaces for Asynchronous Navigation Support

“…These findings have inspired researchers to apply the concept of stigmergy in different human domains such as artificial intelligence and robotics [10,19]. One example is Amazon.com's use of collaborative filtering (CF) technique that allow humans to leave digital traces that can be tracked, analysed and aggregated [29].…”

From Opinions to Recommendations

“…This content is typically noisy and unstructured but it has the potential to act as a rich source of user opinions about products and services. If we can mine these opinions then we may be able to harness them for a new form of recommender system (see also Chapter 5 of this book [21] for a discussion on social navigation). To do this researchers have been turning their attention to developing techniques for mining user-generated content to, for example, identify opinion sentiment, identify product features, and even combining sentiment and features to generate richly opinionated product descriptions and user profiles that can be used in recommendation.…”