Mjt Reinders

TRIBLER: a social-based peer-to-peer system

Most current peer‐to‐peer (P2P) file‐sharing systems treat their users as anonymous, unrelated entities, and completely disregard any social relationships between them. However, social phenomena such as friendship and the existence of communities of users with similar tastes or interests may well be exploited in such systems in order to increase their usability and performance. In this paper we present a novel social‐based P2P file‐sharing paradigm that exploits social phenomena by maintaining social networks and using these in content discovery, content recommendation, and downloading. Based on this paradigms main concepts such as taste buddies and friends, we have designed and implemented the TRIBLER P2P file‐sharing system as a set of extensions to BitTorrent. We present and discuss the design of TRIBLER, and we show evidence that TRIBLER enables fast content discovery and recommendation at a low additional overhead, and a significant improvement in download performance. Copyright

The influence of personalization on tag query length in social media search

Social content systems contain enormous collections of unstructured user-generated content, annotated by the collaborative effort of regular Internet users. Tag-clouds have become popular interfaces that allow users to query the database of these systems by clicking relevant terms. However, these single click queries are often not expressive enough to effectively retrieve the desired content. Users have to use multiple clicks or type longer queries to satisfy their information need. To enhance the predicted content ranking we use a random walk model that effectively integrates the users preference and semantically related query terms. We use the collaborative annotations from a popular on-line book catalog to create a social annotation graph and study the effect of personalization and smoothing for increasing query lengths. We show that personalization and smoothing allow the user to find equally relevant content with fewer query terms compared to a frequency based content ranking with TF-IDF weighing. As expected, we see that the influence of the random walk model disappears if users type more detailed queries. Finally, we discuss the observations with respect to synonyms and homographs which are well known to hamper the performance of information retrieval systems.