We consider the problem of resource selection in clustered Peer-to-Peer Information Retrieval (P2P IR) networks with cooperative peers. The clustered P2P IR framework presents a significant departure from general P2P IR architectures by employing clustering to ensure content coherence between resources at the resource selection layer, without disturbing document allocation. We propose that such a property could be leveraged in resource selection by adapting well-studied and popular inverted lists for centralized document retrieval. Accordingly, we propose the Inverted PeerCluster Index (IPI), an approach that adapts the inverted lists, in a straightforward manner, for resource selection in clustered P2P IR. IPI also encompasses a strikingly simple peer-specific scoring mechanism that exploits the said index for resource selection. Through an extensive empirical analysis on P2P IR testbeds, we establish that IPI competes well with the sophisticated state-of-the-art methods in virtually every parameter of interest for the resource selection task, in the context of clustered P2P IR.
|Title of host publication||Proceedings of the 28th International Conference on Tools with Artificial Intelligence (ICTAI 2016)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - 16 Jan 2017|
|Event||ICTAI 2016: 28th IEEE International Conference on Tools with Artificial Intelligence - USA, San Jose, United States|
Duration: 06 Nov 2016 → 08 Nov 2016
|Name||International Conference on Tools with Artificial Intelligence (ICTAI)|
|Period||06/11/2016 → 08/11/2016|