Inbal Ronen

Social media recommendation based on people and tags

We study personalized item recommendation within an enterprise social media application suite that includes blogs, bookmarks, communities, wikis, and shared files. Recommendations are based on two of the core elements of social media - people and tags. Relationship information among people, tags, and items, is collected and aggregated across different sources within the enterprise. Based on these aggregated relationships, the system recommends items related to people and tags that are related to the user. Each recommended item is accompanied by an explanation that includes the people and tags that led to its recommendation, as well as their relationships with the user and the item. We evaluated our recommender system through an extensive user study. Results show a significantly better interest ratio for the tag-based recommender than for the people-based recommender, and an even better performance for a combined recommender. Tags applied on the user by other people are found to be highly effective in representing that users topics of interest.

Personalized recommendation of social software items based on social relations

We study personalized recommendation of social software items, including bookmarked web-pages, blog entries, and communities. We focus on recommendations that are derived from the users social network. Social network information is collected and aggregated across different data sources within our organization. At the core of our research is a comparison between recommendations that are based on the users familiarity network and his/her similarity network. We also examine the effect of adding explanations to each recommended item that show related people and their relationship to the user and to the item. Evaluation, based on an extensive user survey with 290 participants and a field study including 90 users, indicates superiority of the familiarity network as a basis for recommendations. In addition, an important instant effect of explanations is found - interest rate in recommended items increases when explanations are provided.

Do you know?: recommending people to invite into your social network

In this paper we describe a novel UI and system for providing users with recommendations of people to invite into their explicit enterprise social network. The recommendations are based on aggregated information collected from various sources across the organization and are displayed in a widget, which is part of a popular enhanced employee directory. Recommended people are presented one by one, with detailed reasoning as for why they were recommended. Usage results are presented for a period of four months that indicate an extremely significant impact on the number of connections created in the system. Responses in the organizations blogging system, a survey with over 200 participants, and a set of interviews we conducted shed more light on the way the widget is used and implications of the design choices made.

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.

Diversity among enterprise online communities: collaborating, teaming, and innovating through social media

There is a growing body of research into the adoption and use of social software in enterprises. However, less is known about how groups, such as communities, use and appropriate these technologies, and the implications for community structures. In a study of 188 very active online enterprise communities, we found systematic differences in size, demographics and participation, aligned with differences in community types. Different types of communities differed in their appropriation of social software tools to create and use shared resources, and build relationships. We propose implications for design of community support features, services for potential community members, and organizations looking to derive value from online groups.

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.

Social networks and discovery in the enterprise (SaND)

Traditional information discovery methods are based on content: documents, terms, and the relationships between them. In Web 2.0, people come into play as they create documents and tags in many forms. Personalized search, social graphs, content and people recommendation, are some of the tasks that can take advantage of this newly formed ecosystem. The Social Networks and Discovery (SaND) platform is an aggregation tool for information discovery and analysis over social data gathered from Web 2.0 applications in the enterprise. It leverages complex relationships between content and people as surfaced through the social applications to unleash the value of information. Its integrated index supports combining content-based analysis and people-based analysis over a rich data foundation. Enterprise social data is easily modeled and ingested into SaND, and can be further combined with data from external social applications. This demo will present three main functions provided by SaND: Social search: SaND supports search over the social data using a unified approach [1] in which all system entities (documents, people, tags) are searchable and retrievable (See Figure 1). The search UI enables the searcher to get a wider view on the query topic through results from all entity types, while uncovering the relationships between the on-screen entities. Entity recommendation: SaND can be utilized to recommend people and content for the searcher (Figure 2 shows the “Do You Know” widget for people recommendation). People are recommended according to their “social similarity” relations with the searcher, e.g. organizational and friending ties, similar tagging activity and more. Similarly, content that is related to people that are “socially related” to the searcher is recommended as valuable interesting data. Personalization: Search results are personalized by considering the relations of retrieved entities with the searcher. Entities are ranked according to their relevance to the query as well as according to their relationship strength with the searcher.

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.

Digital Traces of Interest: Deriving Interest Relationships from Social Media Interactions

Facebook and Twitter have changed the way we consume information, allowing the people we follow to become our “social filters” and determine the content of our information stream. The capability to discover the individuals a user is most interested in following has therefore become an important aspect of the struggle against information overflow. We argue that the people users are most interested in following are not necessarily those with whom they are most familiar. We compare these two types of social relationships – interest and familiarity – inside IBM. We suggest inferring interest relationships from users’ public interactions on four enterprise social media applications. We study these interest relationships through an offline analysis as well as an extensive user study, in which we combine people-based and content-based evaluations. The paper reports a rich set of results, comparing various sources for implicit interest indications; distinguishing between content-related activities and status or network updates, showing that the former are of more interest; and highlighting that the interest relationships include very interesting individuals that are not among the most familiar ones, and can therefore play an important role in social stream filtering, especially for content-related activities.