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Social information access is defined as a stream of research that explores methods for organizing the past interactions of users in a community in order to provide future users with better access to information. Social information access covers a wide range of different technologies and strategies that operate on a different scale, which can range from a small closed corpus site to the whole Web.

The 16 chapters included in this book provide a broad overview of modern research on social information access. In order to provide a balanced coverage, these chapters are organized by the main types of information access (i.e., social search, social navigation, and recommendation) and main sources of social information.



Introduction to Social Information Access

This chapter offers an introduction to the emerging field of social information access. Social information access focuses on technologies that organize users past interaction with information in order to provide future users with better access to information. These technologies have become increasingly more popular in all areas of information access, including search, browsing, and recommendation. Starting with a definition of the new field and a brief history of social information access, this chapter introduces a multi-aspect classification of social information access technologies. The two important factors for our classification are the types of information access involved and the source of the social information that has been leveraged to support information access. These two factors are the angles we use in this chapter to create a map of the field, as well as to introduce the book structure and the role of the remaining book chapters in covering social information access topics and technologies.
Peter Brusilovsky, Daqing He

Privacy in Social Information Access

Social information access (SIA) systems crucially depend on user-provided information, and must therefore provide extensive privacy provisions to encourage users to share their personal data. Even though the information SIA systems use is usually considered public, they often use this information in novel ways, and the outcomes of this process may at times lead to unintended consequences for their users’ privacy. Indeed, even if a SIA system is deemed generally beneficial, privacy concerns can play a limiting role in its adoption. This chapter analyzes the privacy implications of several types of SIA systems (aggregators, public content systems, and social network-based systems) from various angles, and discusses a wide range of solutions (both technical and decision-support solutions) to potential privacy threats. Acknowledging that SIA systems are not just a threat to users’ privacy, the chapter concludes with a discussion of the use of social information access as a solution to privacy threats, i.e. by using it to provide social justifications, or by means of adaptive privacy decision support.
Bart P. Knijnenburg

Social Q&A

Social questioning and answering (social Q&A or SQA) is a community-based online service on which peer users ask and answer questions to and for one another about various topics in everyday life. Social Q&A has been labeled with several variations, such as community Q&A, collaborative Q&A, and online Q&A, but it most often refers to a free and open Q&A site with dedicated users who subscribe to the service to ask and answer questions. This encourages people to bring up their various issues, to actively seek solutions and suggestions, and to share personal experiences as well as to give and receive social and emotional support. This chapter provides a literature review of the recent social Q&A research and explains the theories and methods that have been applied to conducting social Q&A research with examples from previous studies in order to show a range of diverse approaches to examining user behaviors and interactions in social Q&A.
Sanghee Oh

Collaborative Information Search

In this chapter, we present one type of social information access called Collaborative Information Search (CIS), where multiple people directly work as a team to collaborate explicitly to search relevant information for resolving a share information need. CIS integrates team collaboration with exploratory search, so that complex search tasks can be decomposed into simpler and smaller tasks for individual team members to resolve. In this chapter, we cover various factors that influence people’s collaboration in search, and discuss the approaches that researchers have developed to support various forms of collaborative information search on the web, in academic setting, and in other environments. We will further talk about the evaluation of collaborative search systems, and then conclude with discussions on the remaining challenges and possible new directions on this topic.
Zhen Yue, Daqing He

Social Navigation

In this chapter we present one of the pioneer approaches in supporting users in navigating the complex information spaces, social navigation support. Social navigation support is inspired by 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 on various approaches in implementing social navigation support in the information space as we also 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 example 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 more recent direction of social navigation support. Furthermore, as social navigation support has been an inspirational approach to 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.
Rosta Farzan, Peter Brusilovsky

Tag-Based Navigation and Visualization

Allowing users to organize content by tagging resources in webbased systems has led to the emergence of the so-called SocialWeb. Tags turned out to be helpful not only for giving recommendations and improving search in social tagging systems but also for enhancing information access by navigating. In this chapter, we will cover much of the pioneer research work that has studied tag-based navigation and visualization. After giving a short overview of the social tagging process and its specifics, we provide an extensive description of the typical user interfaces and visualization techniques characteristic for social tagging systems. As the efficiency of tag-based navigation depends on structuring tagging data, we also provide a review of the state of the art algorithms for tag clustering. Before we conclude, we demonstrate how tag-based navigation can be modeled and discuss the intrinsic navigability of social tagging systems from various theoretic perspectives.
Dimitar Dimitrov, Denis Helic, Markus Strohmaier

Social Search

Today, most people find what they are looking for online by using search engines such as Google, Bing, or Baidu. Modern web search engines have evolved from their roots in information retrieval to developing new ways to cope with the unique nature of web search. In this chapter, we review recent research that aims to make search a more social activity by combining readily available social signals with various strategies for using these signals to influence or adapt more conventional search results. The chapter begins by framing the social search landscape in terms of the sources of data available and the ways in which this can be leveraged before, during, and after search. This includes a number of detailed case studies that serve to mark important milestones in the evolution of social search research and practice.
Peter Brusilovsky, Barry Smyth, Bracha Shapira

Network-Based Social Search

With the wide adoption of social media in recent years, researchers on social information access are gaining more interests on applying various of social interactions (e.g., friendship, bookmarking, tagging) for satisfying people’s information needs. In this chapter, we focus on methods and technologies to boost information retrieval performance based on the idea of representing social information as networks. We study three different types of networks: people-centric networks, document-centric networks and heterogeneous networks combining both. Information from these networks has been utilized to compute vertex similarity (at the individual level), identify network clusters (at the community level) and calculate entire network measurements (at the network level), which are further applied to help search problems not only for seeking documents but also when searching for people. This chapter provides an extensive reviews of existing methods and technologies for performing such two search topics using networks. Through this chapter, our goal is to provide readers with introductory review of the existing work, and provide concrete presentations of relevant technologies for designing and developing network-based social search systems. Finally, we also point out potential remaining challenges on this topic.
Shuguang Han, Daqing He

Accessing Information with Tags: Search and Ranking

With the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.
In this article we review several aspects of the tagging process and its role for accessing information using search and ranking in tagging systems. A literature review of existing work in this area will be complemented by case studies which showcase findings of our own research. We will start with discussing typical properties of tagging systems, present example systems and their typical functionality, their strengths and weaknesses, the users’ motivations, and different types of tags and annotators. To get an understanding of search and ranking methods, we use the formalization of tagging systems as a tripartite graph of users, tags, and resources – known as folksonomy – and discuss its network properties.
Ranking in folksonomies is a core component of information access in such systems. We review two central algorithms, FolkRank and Adjusted Hits, before focussing on a tighter integration of Web search and folksonomies. For this, we compare search in standard search engines with tag-based search, review Social PageRank, a method for ranking web pages that is using the information of tagging systems, and discuss learning-to-rank methods which also utilize tags to improve the ranking of web pages. Finally, we present the concept of logsonomies which provide a unified view on search and tagging by considering clicks on search results as an implicit tagging process. Concluding, we discuss future options for a tighter integration of tagging and search with the goal of improving information access based on user provided content.
Beate Navarro Bullock, Andreas Hotho, Gerd Stumme

Rating-Based Collaborative Filtering: Algorithms and Evaluation

Recommender systems help users find information by recommending content that a user might not know about, but will hopefully like. Rating-based collaborative filtering recommender systems do this by finding patterns that are consistent across the ratings of other users. These patterns can be used on their own, or in conjunction with other forms of social information access to identify and recommend content that a user might like. This chapter reviews the concepts, algorithms, and means of evaluation that are at the core of collaborative filtering research and practice. While there are many recommendation algorithms, the ones we cover serve as the basis for much of past and present algorithm development. After presenting these algorithms we present examples of two more recent directions in recommendation algorithms: learning-to-rank and ensemble recommendation algorithms. We finish by describing how collaborative filtering algorithms can be evaluated, and listing available resources and datasets to support further experimentation. The goal of this chapter is to provide the basis of knowledge needed for readers to explore more advanced topics in recommendation.
Daniel Kluver, Michael D. Ekstrand, Joseph A. Konstan

Recommendations Based on Social Links

The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues, as well as future directions for research. Among several kinds of social recommendations, this chapter focuses on recommendations, which are based on users’ self-defined (i.e., explicit) social links and suggest items, rather than people of interest. The chapter starts by reviewing the needs for social link-based recommendations and studies that explain the viability of social networks as useful information sources. Following that, the core part of the chapter dissects and examines modern research on social link-based recommendations along several dimensions. It concludes with a discussion of several important issues and future directions for social link-based recommendation research.
Danielle Lee, Peter Brusilovsky

Tag-Based Recommendation

Social tagging is an information classification paradigm where the users themselves are given the power to describe and categorize content for their own purposes using tags. The popularity of social tagging, and the ease with which such tags can be generated, assigned, and collected, has sparked significant research interest in tags and their possible applications. One such application is tag-based recommendation: generating better recommendations by incorporating tags into the recommendation process. This chapter provides an overview of the state-of-the-art approaches to tag-based item recommendation, organised by the class of recommendation algorithms that is augmented with tags, such as collaborative filtering, dimensionality reduction, graph-based recommendation, content-based filtering, machine learning, and hybrid recommendation. The chapter also offers an overview of the most important methods for recommending which tags to apply to content. Finally, the chapter discusses the open research problems in tag-based recommendation and what would be needed to address them.
Toine Bogers

From Opinions to Recommendations

Traditionally, recommender systems have relied on user preference data (such as ratings) and product descriptions (such as meta-data) as primary sources of recommendation knowledge. More recently, new sources of recommendation knowledge in the form of social media information and other kinds of user-generated content have emerged as viable alternatives. For example, services such as Twitter, Facebook, Amazon and TripAdvisor provide a rich source of user opinions, positive and negative, about a multitude of products and services. They have the potential to provide recommender systems with access to the fine-grained opinions of real users based on real experiences. This chapter will explore how product opinions can be mined from such sources and can be used as the basis for recommendation tasks. We will draw on a number of concrete case-studies to provide different examples of how opinions can be extracted and used in practice.
Michael P. O’Mahony, Barry Smyth

Recommending Based on Implicit Feedback

Recommender systems have shown to be valuable tools for filtering, ranking, and discovery in a variety of application domains such as e-commerce, media repositories or document-based information in general that includes the various scenarios of Social Information Access discussed in this book. One key to the success of such systems lies in the precise acquisition or estimation of the user’s preferences. While general recommender systems research often relies on the existence of explicit preference statements for personalization, such information is often very sparse or unavailable in real-world applications. Information that allows us to assess the relevance of certain items indirectly through a user’s actions and behavior (implicit feedback) is in contrast often available in abundance. In this chapter we categorize different types of implicit feedback and review their use in the context of recommender systems and Social Information Access applications. We then extend the categorization scheme to be suitable to recent application domains. Finally, we present state-of-the-art algorithmic approaches, discuss challenges when using implicit feedback signals in particular with respect to popularity biases, and discuss selected recent works from the literature.
Dietmar Jannach, Lukas Lerche, Markus Zanker

People Recommendation on Social Media

The social web has brought about many new types of recommender systems. One of the most important is recommendation of people, which bears many unique characteristics and challenges. In this chapter, we will review much of the research that has studied people recommendation in social media. The three main types of people recommendation are based on the presumed level of relationship of the user with the recommended individuals and thereby the goal of the recommendation: from recommending familiar people the user may invite to their network or meet at a place, through recommending interesting people the user may subscribe to or follow, to recommending similar people the user may want to get familiarize with. We will demonstrate each of these recommendation types and the techniques used to address them through different case studies. We will also discuss related research areas, summarize key aspects, and suggest future directions.
Ido Guy

Location Recommendation with Social Media Data

Smartphones with inbuilt location-sensing technologies are now creating a new realm for recommender systems research and pratice. In this chapter, we focus on recommender systems that use location data to help users navigate the physical world. We examine various recommendation problems: recommending new places, recommending the next place to visit, events to attend, and recommending neighbourhoods or large areas to explore further. Lastly, we discuss how (personalized) place search is analogous to web search. For each of these domains, we present relevant data, algorithms, and methods, and we illustrate how researchers are investigating them with examples from the literature. We close by summarizing key aspects and suggesting future directions.
Cécile Bothorel, Neal Lathia, Romain Picot-Clemente, Anastasios Noulas


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