Gossip Learning with Linear Models on Fully Distributed Data

Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications. In this model we have one data record at each network node, but without the possibility to move raw data due to privacy considerations. For example, user profiles, ratings, history, or sensor readings can represent this case. This problem is difficult, because there is no possibility to learn local models, the system model offers almost no guarantees for reliability, yet the communication cost needs to be kept low. Here we propose gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods. We present an instantiation of this approach for the case of classification with linear models. Our main contribution is an ensemble learning method which---through the continuous combination of the models in the network---implements a virtual weighted voting mechanism over an exponential number of models at practically no extra cost as compared to independent random walks. We prove the convergence of the method theoretically, and perform extensive experiments on benchmark datasets. Our experimental analysis demonstrates the performance and robustness of the proposed approach.Comment: The paper was published in the journal Concurrency and Computation: Practice and Experience http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291532-0634 (DOI: http://dx.doi.org/10.1002/cpe.2858). The modifications are based on the suggestions from the reviewer

A hybrid peer-to-peer recommendation system architecture based on locality-sensitive hashing

Recommendation systems have become ubiquitous recently as they help to mitigate information overflow of the nowadays life. The vast majority of current recommendation system approaches are centralized. Although centralized recommendations have several significant advantages, they also have two main drawbacks: single point of failure and the necessity for users to share their preferences. In this paper, a system architecture of a peer-to-peer recommendation system with limited preferences disclosure is proposed. The proposed architecture is based on a locality-sensitive hashing of user preferences and an anonymized distributed hash table approach to peer-to-peer design

Collaborative Filtering Using Random Neighbours in Peer-to-Peer Networks

Traditionally, collaborative filtering (CF) algorithms used for recommendation operate on complete knowledge. This makes these algorithms hard to employ in a decentralized context where not all users ’ ratings can be available at all locations. In this paper we investigate how the well-known neighbourhood-based CF algorithm by Herlocker et al. [5] operates on partial knowledge; that is, how many similar users does the algorithm actually need to produce good recommendations for a given user, and how similar must those users be. We show for the popular MovieLens 1,000,000 and Jester datasets that sufficiently good recommendations can be made based on the ratings of a neighbourhood consisting of a relatively small number of randomly selected users