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.

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.