Recommending in an Enterprise Social Media Stream without Explicit User Feedback

Social Media Streams allow users to share user-generated content as well as aggregate different streams into one single stream. Additional Enterprise Social Media Streams organize the stream messages into projects with different usage patterns compared to public collaboration platforms such as Twitter. The aggregated stream helps the user to access the information in one single place but also leads to an information overload. Here, a recommendation engine can help to distinguish between relevant and irrelevant information for the users. In previous work we showed how features inferred from messages can predict relevant information and can be used to learn a user model. In this paper we show how this approach can be used in a productive enterprise social media stream application without using explicit user feedback. We develop a time binned evaluation measure which suits the scenario to steadily recommend messages of the stream. Finally, we evaluate our algorithm in different variations and show that it helps to identify relevant messages

Beyond Personalization: Research Directions in Multistakeholder Recommendation

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.Comment: 64 page

Recommendation in Enterprise 2.0 Social Media Streams

A social media stream allows users to share user-generated content as well as aggregate different external sources into one single stream. In Enterprise 2.0 such social media streams empower co-workers to share their information and to work efficiently and effectively together while replacing email communication. As more users share information it becomes impossible to read the complete stream leading to an information overload. Therefore, it is crucial to provide the users a personalized stream that suggests important and unread messages. The main characteristic of an Enterprise 2.0 social media stream is that co-workers work together on projects represented by topics: the stream is topic-centered and not user-centered as in public streams such as Facebook or Twitter. A lot of work has been done dealing with recommendation in a stream or for news recommendation. However, none of the current research approaches deal with the characteristics of an Enterprise 2.0 social media stream to recommend messages. The existing systems described in the research mainly deal with news recommendation for public streams and lack the applicability for Enterprise 2.0 social media streams. In this thesis a recommender concept is developed that allows the recommendation of messages in an Enterprise 2.0 social media stream. The basic idea is to extract features from a new message and use those features to compute a relevance score for a user. Additionally, those features are used to learn a user model and then use the user model for scoring new messages. This idea works without using explicit user feedback and assures a high user acceptance because no intense rating of messages is necessary. With this idea a content-based and collaborative-based approach is developed. To reflect the topic-centered streams a topic-specific user model is introduced which learns a user model independently for each topic. There are constantly new terms that occur in the stream of messages. For improving the quality of the recommendation (by finding more relevant messages) the recommender should be able to handle the new terms. Therefore, an approach is developed which adapts a user model if unknown terms occur by using terms of similar users or topics. Also, a short- and long-term approach is developed which tries to detect short-term interests of users. Only if the interest of a user occurs repeatedly over a certain time span are terms transferred to the long-term user model. The approaches are evaluated against a dataset obtained through an Enterprise 2.0 social media stream application. The evaluation shows the overall applicability of the concept. Specifically the evaluation shows that a topic-specific user model outperforms a global user model and also that adapting the user model according to similar users leads to an increase in the quality of the recommendation. Interestingly, the collaborative-based approach cannot reach the quality of the content-based approach

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Understanding eINVs through the lens of prior research in entrepreneurship, international business and international entrepreneurship

In this chapter we examine the growing phenomenon of internet-based international new ventures, which we label “eINVS,” through the lens of previous research in the fields of entre- preneurship, international business and international entrepreneurship. Our purpose is to iden- tify where these existing bodies of research help us to understand eINVs, and where there are gaps that constitute important questions for future research. We define an eINV by adapting a widely used definition of international new ventures (INV) (Oviatt and McDougall 2005: 5): an eINV is a venture whose business model is enabled by a digital platform and that, from incep- tion, seeks to derive significant competitive advantage from international growth. With a focus explicitly on how extant research helps us understand eINVs, this review differs from that of Reuber and Fischer (2011b), who focus on firm-level internet-related resources that are related to the internationalization of ventures in general; that of Pezderka and Sinkovics (2011), who focus on risk and the online foreign market entry decisions of small and medium-sized enter- prises (SMEs); and that of Chandra and Coviello (2010), who focus on consumers using the internet to pursue international opportunities

Closing Information Gaps with Need-driven Knowledge Sharing

Informationslücken schließen durch bedarfsgetriebenen Wissensaustausch Systeme zum asynchronen Wissensaustausch – wie Intranets, Wikis oder Dateiserver – leiden häufig unter mangelnden Nutzerbeiträgen. Ein Hauptgrund dafür ist, dass Informationsanbieter von Informationsuchenden entkoppelt, und deshalb nur wenig über deren Informationsbedarf gewahr sind. Zentrale Fragen des Wissensmanagements sind daher, welches Wissen besonders wertvoll ist und mit welchen Mitteln Wissensträger dazu motiviert werden können, es zu teilen. Diese Arbeit entwirft dazu den Ansatz des bedarfsgetriebenen Wissensaustauschs (NKS), der aus drei Elementen besteht. Zunächst werden dabei Indikatoren für den Informationsbedarf erhoben – insbesondere Suchanfragen – über deren Aggregation eine fortlaufende Prognose des organisationalen Informationsbedarfs (OIN) abgeleitet wird. Durch den Abgleich mit vorhandenen Informationen in persönlichen und geteilten Informationsräumen werden daraus organisationale Informationslücken (OIG) ermittelt, die auf fehlende Informationen hindeuten. Diese Lücken werden mit Hilfe so genannter Mediationsdienste und Mediationsräume transparent gemacht. Diese helfen Aufmerksamkeit für organisationale Informationsbedürfnisse zu schaffen und den Wissensaustausch zu steuern. Die konkrete Umsetzung von NKS wird durch drei unterschiedliche Anwendungen illustriert, die allesamt auf bewährten Wissensmanagementsystemen aufbauen. Bei der Inversen Suche handelt es sich um ein Werkzeug das Wissensträgern vorschlägt Dokumente aus ihrem persönlichen Informationsraum zu teilen, um damit organisationale Informationslücken zu schließen. Woogle erweitert herkömmliche Wiki-Systeme um Steuerungsinstrumente zur Erkennung und Priorisierung fehlender Informationen, so dass die Weiterentwicklung der Wiki-Inhalte nachfrageorientiert gestaltet werden kann. Auf ähnliche Weise steuert Semantic Need, eine Erweiterung für Semantic MediaWiki, die Erfassung von strukturierten, semantischen Daten basierend auf Informationsbedarf der in Form strukturierter Anfragen vorliegt. Die Umsetzung und Evaluation der drei Werkzeuge zeigt, dass bedarfsgetriebener Wissensaustausch technisch realisierbar ist und eine wichtige Ergänzung für das Wissensmanagement sein kann. Darüber hinaus bietet das Konzept der Mediationsdienste und Mediationsräume einen Rahmen für die Analyse und Gestaltung von Werkzeugen gemäß der NKS-Prinzipien. Schließlich liefert der hier vorstellte Ansatz auch Impulse für die Weiterentwicklung von Internetdiensten und -Infrastrukturen wie der Wikipedia oder dem Semantic Web