Abstract: The users of social network sites are always constructing a large network, recommending friends to registered users is a crucial task for these sites. Traditional content- based or collaborative filtering recommend technologies are limited for the task, because the users and items are the same dataset, which have rich attributes and complex social graph. In the paper, we proposed a local random walk and random forests combined method to do friends recommendation for large social networks. The experimental results show that the method has high precision and high recall at the same time.

• Publication Date: 17-Nov-2014
• DOI: 10.15224/978-1-63248-051-4-91
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Abstract: A MAC (Medium Access Control) layer throughput is evaluated over error-prone channel in the IEEE 802.11ac-based wireless LAN. In this evaluation, DCF (Distributed Coordination Function) protocol and A-MPDU (MAC Protocol Data Unit Aggregation) scheme are used. Using theoretical analysis method, the MAC saturation throughput is evaluated with the PER (Packet Error Rate) on the condition that the number of station, transmission probability, the number of parallel beams and the number of frames in each A-MPDU are variables. When the PER is 10-2 and the number of aggregated MPDUs in each A-MPDU is 20, it is identified that the MAC layer throughput of IEEE 802.11ac can be maximally attained up to a 92.8% of physical transmission rate in this evaluation

• Publication Date: 26-Oct-2014
• DOI: 10.15224/978-1-63248-029-3-70
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