Michal Jacovi

The shark-search algorithm. An application: tailored Web site mapping

Abstract This paper introduces the “shark search” algorithm, a refined version of one of the first dynamic Web search algorithms, the “fish search”. The shark-search has been embodied into a dynamic Web site mapping that enables users to tailor Web maps to their interests. Preliminary experiments show significant improvements over the original fish-search algorithm.

Harvesting with SONAR: the value of aggregating social network information

Web 2.0 gives people a substantial role in content and metadata creation. New interpersonal connections are formed and existing connections become evident through Web 2.0 services. This newly created social network (SN) spans across multiple services and aggregating it could bring great value. In this work we present SONAR, an API for gathering and sharing SN information. We give a detailed description of SONAR, demonstrate its potential value through user scenarios, and show results from experiments we conducted with a SONAR-based social networking application. These suggest that aggregating SN information across diverse data sources enriches the SN picture and makes it more complete and useful for the end user.

Same places, same things, same people?: mining user similarity on social media

In this work we examine nine different sources for user similarity as reflected by activity in social media applications. We suggest a classification of these sources into three categories: people, things, and places. Lists of similar people returned by the nine sources are found to be highly different from each other as well as from the list of people the user is familiar with, suggesting that aggregation of sources may be valuable. Evaluation of the sources and their aggregates points at their usefulness across different scenarios, such as information discovery and expertise location, and also highlights sources and aggregates that are particularly valuable for inferring user similarity.

Mining expertise and interests from social media

The rising popularity of social media in the enterprise presents new opportunities for one of the organizations most important needs--expertise location. Social media data can be very useful for expertise mining due to the variety of existing applications, the rich metadata, and the diversity of user associations with content. In this work, we provide an extensive study that explores the use of social media to infer expertise within a large global organization. We examine eight different social media applications by evaluating the data they produce through a large user survey, with 670 enterprise social media users. We distinguish between two semantics that relate a user to a topic: expertise in the topic and interest in it and compare these two semantics across the different social media applications.

Increasing engagement through early recommender intervention

Social network sites rely on the contributions of their members to create a lively and enjoyable space. Recent research has focused on using personalization and recommender technologies to encourage participation of existing members. In this work we present an early-intervention approach to encouraging participation and engagement, which makes recommendations to new users during their sign-up process. Our recommender system exploits external social media to produce people and profile entry recommendations for new users. We present results of a live user study, showing that users who received recommendations at sign-up created more social connections, contributed more content, and were on the whole more engaged with the system, contributing more without prompt and returning more often. We further show that recommendations for multiple content types yield significantly better results, in terms of user contribution and consumption; and that recommendations of more active users yield a higher return rate.

Ask before you search: peer support and community building with reachout

This paper presents ReachOut, a chat-based tool for peer support, collaboration, and community building. We describe the philosophy behind the tool and explain how posting questions in the open directly benefits the creation, distribution, and use of organizational knowledge, in addition to enhancing the cohesion of the community involved. ReachOut proposes new methods of handling problems that include locating, selecting, and approaching the right set of potential advisers. We discuss the advantages of public discussions over private, one-on-one sessions, and how this is enhanced by our unique combination of synchronous and asynchronous communication. We present and analyze results from a pilot of ReachOut and conclude with plans for future research and development.

Adding support for dynamic and focused search with Fetuccino

Abstract This paper proposes two enhancements to existing search services over the Web. One enhancement is the addition of limited dynamic search around results provided by regular Web search services, in order to correct part of the discrepancy between the actual Web and its static image as stored in search repositories. The second enhancement is an experimental two-phase paradigm that allows the user to distinguish between a domain query and a focused query within the dynamically identified domain. We present Fetuccino, an extension of the Mapuccino system that implements these two enhancements. Fetuccino provides an enhanced user-interface for visualization of search results, including advanced graph layout, display of structural information and support for standards (such as XML). While Fetuccino has been implemented on top of existing search services, its features could easily be integrated into any search engine for better performance. A light version of Fetuccino is available on the Internet at http://www.ibm.com/java/fetuccino.

Do you want to know?: recommending strangers in the enterprise

Recent studies on people recommendation have focused on suggesting people the user already knows. In this work, we use social media behavioral data to recommend people the user is not likely to know, but nonetheless may be interested in. Our evaluation is based on an extensive user study with 516 participants within a large enterprise and includes both quantitative and qualitative results. We found that many employees valued the recommendations, even if only one or two of nine recommendations were interesting strangers. Based on these results, we discuss potential deployment routes and design implications for a stranger recommendation feature.

The chasms of CSCW: a citation graph analysis of the CSCW conference

The CSCW conference is celebrating its 20th birthday. This is a perfect time to analyze the coherence of the field, to examine whether it has a solid core or sub-communities, and to identify various patterns of its development. In this paper we analyze the structure of the CSCW conference using structural analysis of the citation graph of CSCW and related publications. We identify the conferences core and most prominent clusters. We also define a measure to identify chasm-papers, namely papers cited significantly more outside the conference than within, and analyze such papers.

Public vs. private: comparing public social network information with email

The goal of this research is to facilitate the design of systems which will mine and use sociocentric social networks without infringing privacy. We describe an extensive experiment we conducted within our organization comparing social network information gathered from various intranet public sources with social network information gathered from a private source - the organizational email system. We also report the conclusions of a series of interviews we conducted based on our experiment. The results shed light on the richness of public social network information, its characteristics, and added value over email network information.