[Show abstract][Hide abstract] ABSTRACT: In this paper we present TROOTH as a robust, partially decentralized, collaborative, and personalized recommendation system. We examine a voting scheme where users have to assess a continuous stream of items to be either good or bad-either manually or, assisted by TROOTH, automatically based on previous votes. For this purpose, TROOTH implicitly creates special interest groups containing users who share similar opinions expressed by assenting votes. To evaluate an item with TROOTH, a user trusts those votes most which have been cast by other users in the same group. TROOTH has been implemented in the SPAMATO spam filter system where it is used in the context of collaborative spam filtering.
[Show abstract][Hide abstract] ABSTRACT: We introduce the Distributed Approximative System Information Service (DASIS) as a useful scheme to aggregate approximative information on the state of a peer-to-peer system. We present how this service can be integrated into existing peer-to-peer systems, such as Kademlia and Chord. As a sample application, we show how DASIS can be employed for establishing an effective deterministic join algorithm. Through simulation, we demonstrate that the insertion of peers using DASIS information results in a well-balanced system. Moreover, our join algorithm gracefully resolves load imbalances in the system due to unfortunate biased leaves of peers.