Naama Zwerdling

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.

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.

Towards expressive exploratory search over entity-relationship data

In this paper we describe a novel approach for exploratory search over rich entity-relationship data that utilizes a unique combination of expressive, yet intuitive, query language, faceted search, and graph navigation. We describe an extended faceted search solution which allows to index, search, and browse rich entity-relationship data. We report experimental results of an evaluation study, using a benchmark of several of entity-relationship datasets, demonstrating that our exploratory approach is both effective and efficient compared to other existing approaches.

Entity oriented search and exploration for cultural heritage collections: the EU cultura project

In this paper we describe an entity oriented search and exploration system that we are developing for the EU Cultura project.

CULTURA: A Metadata-Rich Environment to Support the Enhanced Interrogation of Cultural Collections

The increased digitisation of cultural collections, and their availability on the World Wide Web, has made access to these valuable documents much easier than ever before. However, despite the increased availability of access to cultural archives, curators still struggle to instigate and enhance engagement with these resources. The CULTURA project is actively addressing this issue through the development of a metadata-driven personalisation environment for navigating cultural collections and instigating collaborations. The corpus agnostic CULTURA environment also supports a full spectrum of users: ranging from professional researchers seeking patterns in the data and trying to answer complex queries; to interested members of the public who need help navigating a vast collection of resources. This paper discusses the state of the art in this area and the various innovative approaches used in the CULTURA project, with a special focus on how the underlying metadata helps facilitate its semantically rich environment.

Social Media-Based Expertise Evidence

Social media provides a fertile ground for expertise location. The public nature of the data supports expertise inference with little privacy infringement and, in addition, presentation of direct and detailed evidence for an expert’s skillfulness in the queried topic. In this work, we study the use of social media for expertise evidence. We conducted two user surveys of enterprise social media users within a large global organization, in which participants were asked to rate anonymous experts based on artificial and real evidence originating from different types of social media data. Our results indicate that the social media data types perceived most convincing as evidence are not necessarily the ones from which expertise can be inferred most precisely or effectively. We describe these results in detail and discuss implications for designers and architects of expertise location systems.

Orient Me!: Important Event Identification in an Enterprise Activity Stream

Social media platforms such as blogs, wikis and file sharing have become very popular in enterprises. Despite their effectiveness in increasing collaboration in the organization, employees are overloaded with information originating from these many sources and find it hard to orient themselves in the stream of events occurring in their organizational news feed. In this paper we identify what makes an event in an organizational social media platform important to employees. Once important factors of an event to an employee are identified, the stream of events can be personalized and prioritized based on those and thus reduce the overload and assist in work efficiency. Through interviews and two extensive user surveys, the first hypothetical and the second empirical, we identified which factors of an event make it important and compare results from the hypothetical and empirical surveys.