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Most P2P Video-On-Demand (VOD) schemes mainly focus more on mending service architectures and optimizing overlays but do not carefully consider the user behavior and the benefit of prefetching strategies. As a result, they cannot better support VCR-oriented services in terms of substantive asynchronous clients, and free VCR controls for P2P VODs. In this paper, we propose VOVO, VCR-oriented VOD for large-scale P2P networks. By mining associations inside a video, the segments requested in VCR interactivities are accurately predicted based on the information collected through gossips. Together with a hybrid caching strategy, a collaborative prefetching scheme is proposed to optimize resource distribution among neighboring peers. We evaluate VOVO through extensive experiments. Results show that VOVO is scalable and effective, providing short startup latencies and good performance in VCR interactivities.