Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main contribution is that we have integrated models from previous work in sensemaking and information seeking behavior to present a canonical social model of user activities before, during, and after search, suggesting where in the search process even implicitly shared information may be valuable to individual searchers.
However, in addition to explicit collaboration in joint search tasks @cite_9 , we believe that even implicit social experiences could improve the search process. Therefore, the general term social search'' may more suitably describe information seeking and sensemaking habits that make use of a range of possible social interactions: including searches that utilize social and expertise networks or that may be done in shared social workspaces. This notion certainly encompasses collaborative co-located search, as well as remote and asynchronous collaborative and collective search. Our focus in this paper is to explore a model of social search that may offer suggestions for supporting social interactions in the information seeking process.
@cite_9
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Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main contribution is that we have integrated models from previous work in sensemaking and information seeking behavior to present a canonical social model of user activities before, during, and after search, suggesting where in the search process both explicitly and implicitly shared information may be valuable to individual searchers. Studies of search habits reveal that people engage in many search tasks involving collaboration with others, such as travel planning, organizing social events, or working on a homework assignment. However, current Web search tools are designed for a single user, working alone. We introduce SearchTogether, a prototype that enables groups of remote users to synchronously or asynchronously collaborate when searching the Web. We describe an example usage scenario, and discuss the ways SearchTogether facilitates collaboration by supporting awareness, division of labor, and persistence. We then discuss the findings of our evaluation of SearchTogether, analyzing which aspects of its design enabled successful collaboration among study participants. Search is not just a box and ten blue links. Search is a journey: an exploration where what we encounter along the way changes what we seek. But in order to guide people along this journey, designers must understand both the art and science of search.In Designing the Search Experience, authors Tony Russell-Rose and Tyler Tate weave together the theories of information seeking with the practice of user interface design. Understand how people search, and how the concepts of information seeking, information foraging, and sensemaking underpin the search process. Apply the principles of user-centered design to the search box, search results, faceted navigation, mobile interfaces, social search, and much more. Design the cross-channel search experiences of tomorrow that span desktop, tablet, mobile, and other devices. Table of Contents PART 1 A Framework for Search and Discovery Chapter 1 The User Chapter 2 Information Seeking Chapter 3 Context Chapter 4 Modes of Search and Discovery PART 2 Design Solutions Chapter 5 Formulating the Query Chapter 6 Displaying and Manipulating Results Chapter 7 Faceted Search Chapter 8 Mobile Search Chapter 9 Social Search Part 3 Designing the Future Chapter 10 Cross-Channel Information Interaction The growth of Web 2.0 and fundamental theoretical breakthroughs have led to an avalanche of interest in social networks. This paper focuses on the problem of modeling how social networks accomplish tasks through peer production style collaboration. We propose a general interaction model for the underlying social networks and then a specific model ( i L ink for social search and message routing. A key contribution here is the development of a general learning framework for making such online peer production systems work at scale. The i L ink model has been used to develop a system for FAQ generation in a social network (FAQ tory ), and experience with its application in the context of a full-scale learning-driven workflow application (CALO) is reported. We also discuss methods of adapting i L ink technology for use in military knowledge sharing portals and other message routing systems. Finally, the paper shows the connection of i L ink to SQM, a theoretical model for social search that is a generalization of Markov Decision Processes and the popular Pagerank model. In this paper, we build a social search engine named Glaucus for location-based queries. They compose a significant portion of mobile searches, thus becoming more popular with the prevalence of mobile devices. However, most of existing social search engines are not designed for location-based queries and thus often produce poor-quality results for such queries. Glaucus is inherently designed to support location-based queries. It collects the check-in information, which pinpoints the places where each user visited, from location-based social networking services such as Foursquare. Then, it calculates the expertise of each user for a query by using our new probabilistic model called the location aspect model . We conducted two types of evaluation to prove the effectiveness of our engine. The results showed that Glaucus selected the users supported by stronger evidence for the required expertise than existing social search engines. In addition, the answers from the experts selected by Glaucus were highly rated by our human judges in terms of answer satisfaction. Modern work is a highly social process, offering many cues for people to organize communication and access information. Shared physical workplaces provide natural support for tasks such as (a) social reminding about communication commitments and keeping track of collaborators and friends, and (b) social data mining of local expertise for advice and information. However, many people now collaborate remotely using tools such as email and voicemail. Our field studies show that these tools do not provide the social cues needed for group work processes. In part, this is because the tools are organized around messages, rather than people. In response to this problem, we created ContactMap, a system that makes people the primary unit of interaction. ContactMap provides a structured social desktop representation of users' important contacts that directly supports social reminding and social data mining. We conducted an empirical evaluation of ContactMap, comparing it with traditional email systems, on tasks suggested by our fieldwork. Users performed better with ContactMap and preferred ContactMap for the majority of these tasks. We discuss future enhancements of our system and the implications of these results for future communication interfaces and for theories of mediated communication. Social media is becoming popular these days, where user necessarily interacts with each other to form social networks. Influence network, as one special case of social network, has been recognized as significantly impacting social activities and user decisions. We emphasize in this paper that the inter-user influence is essentially topic-sensitive, as for different tasks users tend to trust different influencers and be influenced most by them. While existing research focuses on global influence modeling and applies to text-based networks, this work investigates the problem of topic-sensitive influence modeling in the multimedia domain. We propose a multi-modal probabilistic model, considering both users' textual annotation and uploaded visual image. This model is capable of simultaneously extracting user topic distributions and topic-sensitive influence strengths. By identifying the topic-sensitive influencer, we are able to conduct applications like collective search and collaborative recommendation. A risk minimization-based general framework for personalized image search is further presented, where the image search task is transferred to measure the distance of image and personalized query language models. The framework considers the noisy tag issue and enables easy incorporation of social influence. We have conducted experiments on a large-scale Flickr dataset. Qualitative as well as quantitative evaluation results have validated the effectiveness of the topic-sensitive influencer mining model, and demonstrated the advantage of incorporating topic-sensitive influence in personalized image search and topic-based image recommendation. Social networks have the surprising property of being “searchable”: Ordinary people are capable of directing messages through their network of acquaintances to reach a specific but distant target person in only a few steps. We present a model that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions. Our model defines a class of searchable networks and a method for searching them that may be applicable to many network search problems, including the location of data files in peer-to-peer networks, pages on the World Wide Web, and information in distributed databases. Interactive tables can enhance small group colocated collaborative work in many domains. One application enabled by this new technology is copresent, collaborative search for digital content. For example, a group of students could sit around an interactive table and search for digital images to use in a report. We have developed TeamSearch, an application that enables this type of activity by supporting group specification of Boolean style queries. We explore whether TeamSearch should consider all group members' activities as contributing to a single query or should interpret them as separate, parallel search requests. The results reveal that both strategies are similarly efficient, but that collective query formation has advantages in terms of enhancing group collaboration and awareness, allowing users to bootstrap query specification skills, and personal preference. This suggests that team centric ills may offer benefits beyond the "staples" of efficiency and result quality that are usually considered when designing search interfaces. Social tagging has become an interesting approach to improve search and navigation over the actual Web, since it aggregates the tags added by different users to the same resource in a collaborative way. This way, it results in a list of weighted tags describing its resource. Combined to a classical taxonomic classification system such as that by Wikipedia, social tags can enhance document navigation and search. On the one hand, social tags suggest alternative navigation ways, including pivot-browsing, popularity-driven navigation, and filtering. On the other hand, it provides new metadata, sometimes uncovered by documents' content, that can substantially improve document search. In this work, the inclusion of an interface to add user-defined tags describing Wikipedia articles is proposed, as a way to improve article navigation and retrieval. As a result, a prototype on applying tags over Wikipedia is proposed in order to evaluate its effectiveness. Emerging social network applications for sharing and collaboration need a way to publish and search a big amount of social objects. Currently, social applications allow the association of a set of user defined keywords, named tags, when publishing these objects, in order to allow searching for them later using a subset of these tags. Commercial systems and recent research community proposals preclude a wide Internet deployment due to the emergence of scalability and hot spot problems in the nodes. We propose T-DHT, an innovative hybrid unstructured-structured DHT based approach, to cope with these high demanding requirements, in a fully scalable, distributed and balanced way. The storage process allows attaching a set of user tags to the stored object and takes at most O(Log(N)) node hops. The tag information attached to the object is stored in a compact way into the node links using a bloom filter, in order to be used later in the search process. The search process allows searching for previously stored objects by means of a tag conjunction and also takes at most O(Log(N)) node hops. The search process is based on DHT typical search combined with an unstructured search algorithm using the tag information previously stored into bloom filters of node links. The simulation results show T-DHT performs in a fully balanced and scalable way, without generating typical hot spot problems even if unbalanced distributions of popular tags are used. Although T-DHT has been devised to build a scalable infrastructure for social applications, it can be applied to solve the more general Peer-to-Peer keyword search problems. In a search task, a group of agents compete to be the first to find the solution. Each agent has different private information to incorporate into its search. This problem is inspired by settings such as scientific research, Bitcoin hash inversion, or hunting for some buried treasure. A social planner such as a funding agency, mining pool, or pirate captain might like to convince the agents to collaborate, share their information, and greatly reduce the cost of searching. However, this cooperation is in tension with the individuals' competitive desire to each be the first to win the search. The planner's proposal should incentivize truthful information sharing, reduce the total cost of searching, and satisfy fairness properties that preserve the spirit of the competition. We design contract-based mechanisms for information sharing without money. The planner solicits the agents' information and assigns search locations to the agents, who may then search only within their assignments. Truthful reporting of information to the mechanism maximizes an agent's chance to win the search. e-voluntary participation is satisfied for large search spaces. In order to formalize the planner's goals of fairness and reduced search cost, we propose a simplified, simulated game as a benchmark and quantify fairness and search cost relative to this benchmark scenario. The game is also used to implement our mechanisms. Finally, we extend to the case where coalitions of agents may participate in the mechanism, forming larger coalitions recursively. Web search is often viewed as a solitary task; however, there are many situations in which groups of people gather around a single computer to jointly search for information online. We present the findings of interviews with teachers, librarians, and developing world researchers that provide details about users' collaborative search habits in shared-computer settings, revealing several limitations of this practice. We then introduce CoSearch, a system we developed to improve the experience of co-located collaborative Web search by leveraging readily available devices such as mobile phones and extra mice. Finally, we present an evaluation comparing CoSearch to status quo collaboration approaches, and show that CoSearch enabled distributed control and division of labor, thus reducing the frustrations associated with shared-computer searches, while still preserving the positive aspects of communication and collaboration associated with joint computer use. This work investigates personalized social search based on the user's social relations -- search results are re-ranked according to their relations with individuals in the user's social network. We study the effectiveness of several social network types for personalization: (1) Familiarity-based network of people related to the user through explicit familiarity connection; (2) Similarity-based network of people "similar" to the user as reflected by their social activity; (3) Overall network that provides both relationship types. For comparison we also experiment with Topic-based personalization that is based on the user's related terms, aggregated from several social applications. We evaluate the contribution of the different personalization strategies by an off-line study and by a user survey within our organization. In the off-line study we apply bookmark-based evaluation, suggested recently, that exploits data gathered from a social bookmarking system to evaluate personalized retrieval. In the on-line study we analyze the feedback of 240 employees exposed to the alternative personalization approaches. Our main results show that both in the off-line study and in the user survey social network based personalization significantly outperforms non-personalized social search. Additionally, as reflected by the user survey, all three SN-based strategies significantly outperform the Topic-based strategy. In the last few years, social tagging systems have become a standard application of the World Wide Web. These systems can be considered as shared external knowledge structures of users on the Internet. In this paper, we describe how social tagging systems relate to individual semantic memory structures and how social tags affect individual processes of learning and information foraging. Furthermore, we present an experimental online study aimed at evaluating this interaction of external and internal structures of spreading activation. We report on effects of social tagging systems as visualized collective knowledge representations on individual processes of information search and learning. GiveALink.org is a social bookmarking site where users may donate and view their personal bookmark files online securely. The bookmarks are analyzed to build a new generation of intelligent information retrieval techniques to recommend, search, and personalize the Web. GiveALink does not use tags, content, or links in the submitted Web pages. Instead we present a semantic similarity measure for URLs that takes advantage both of the hierarchical structure in the bookmark files of individual users, and of collaborative filtering across users. In addition, we build a recommendation and search engine from ranking algorithms based on popularity and novelty measures extracted from the similarity-induced network. Search results can be personalized using the bookmarks submitted by a user. We evaluate a subset of the proposed ranking measures by conducting a study with human subjects. Social networks let the people find and know other people and benefit form their information. Semantic Web standard ontologies support social network sites for making use of other social networks information and hence help their expansion and unification, making them a huge social network. As social networks are public virtual social places much information may exist in them that may not be trustworthy to all. A mechanism in needed to rate coming news, reviews and opinions about a definite subject from users, according to each user preference. There should be a feature for users to specify how much they trust a friend and a mechanism to infer the trust from one user to another that is not directly a friend of the user so that a recommender site can benefit from these trust ratings for showing trustworthy information to each user from her or his point of view from not only her or his directly trusted friends but also the other indirectly trusted users. This work suggests using fuzzy linguistic terms to specify trust to other users and proposes an algorithm for inferring trust from a person to another person that may be not directly connected in the trust graph of a social network. The algorithm is implemented and compared to an algorithm that let the users to specify their trust with a number in a definite range. While according to the imprecise nature of the trust concept writing and reading a linguistic expression for trust is much more natural than a number for users, the results show that the algorithm offers more precise information than the previously used algorithm especially when contradictory beliefs should be composed and also when a more precise inference is potentially possible in searching deeper paths. As the trust graphs and inference are viewed abstractly, they can be well employed in other multi agent systems. Because of intense collaborative needs, requirements engineering is a challenge in global software development. How do distributed teams manage the development of requirements in environments that require significant cross-site collaboration and coordination? In this paper, we report research that used social network analysis to explore collaboration and awareness among team members during requirements management in an industrial distributed software team. Using the lens of a requirements-centred social network to group team members who work on a particular requirement, we collected data to characterize requirements-centric collaborations in a project, and to examine aspects of awareness of requirements changes within these networks. Our findings indicate organic patterns of collaboration involving considerable cross-site interaction, in which communication of changes was the most predominant reason for interaction. Although we did not find evidence that distance affects developers' awareness of remote team members who work on the same requirements, distance affected how accessible the remote colleagues were. We discuss implications for knowledge sharing and coordination of work on a requirement in distributed teams, and propose directions for the design of collaboration tools that support awareness in distributed requirements management. In this paper, we evaluate query term suggestion in the context of academic professional search. Our overall goal is to support scientists in their information seeking tasks. We set up an interactive search system in which terms are extracted from clicked documents and suggested to the user before every query specification step. We evaluated our method with the iSearch collection of academic information seeking behaviour and crowdsourced term relevance judgements. We found that query term suggestion can significantly improve recall in academic search. We present a hybrid method for an expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. Our method aims at improving the accuracy of the recommendation prediction by considering the social aspect of experts' behaviors. For this purpose, social communities of experts are first detected by applying social network analysis and using factors such as experience, background, knowledge level, and personal preferences of experts. Representative members of communities are then identified using a network centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking for an expert, to the representatives of the most relevant community. Further from an expert's perspective, she he has been suggested to work on relevant information items that fall under her his expertise and interests.