Point of interest (POI) recommendation problem in location based social network (LBSN) is of great importance and the challenge lies in the data sparsity, implicit user feedback and personalized preference. To improve the precision of recommendation, a tensor decomposition based collaborative filtering (TDCF) algorithm is proposed for POI recommendation. Tensor decomposition algorithm is utilized to fill the missing values in tensor (user-category-time). Specifically, locations are replaced by location categories to reduce dimension in the first phase, which effectively solves the problem of data sparsity. In the second phase, we get the preference rating of users to POIs based on time and user similarity computation and hypertext induced topic search (HITS) algorithm with spatial constraints, respectively. Finally the user’s preference score of locations are determined by two items with different weights, and the Top-N locations are the recommendation results for a user to visit at a given time. Experimental results on two LBSN datasets demonstrate that the proposed model gets much higher precision and recall value than the other three recommendation methods.
As network traffic bandwidth is increasing at an exponential rate, it’s impossible to keep up with the speed of networks by
just increasing the speed of processors. Besides, increasingly complex intrusion detection methods only add further to the
pressure on network intrusion detection (NIDS) platforms, so the continuous increasing speed and throughput of network poses
new challenges to NIDS. To make NIDS usable in Gigabit Ethernet, the ideal policy is using a load balancer to split the traffic
data and forward those to different detection sensors, which can analyze the splitting data in parallel. In order to make
each slice contains all the evidence necessary to detect a specific attack, the load balancer design must be complicated and
it becomes a new bottleneck of NIDS. To simplify the load balancer this paper put forward a distributed neural network learning
algorithm (DNNL). Using DNNL a large data set can be split randomly and each slice of data is presented to an independent
neural network; these networks can be trained in distribution and each one in parallel. Completeness analysis shows that DNNL’s
learning algorithm is equivalent to training by one neural network which uses the technique of regularization. The experiments
to check the completeness and efficiency of DNNL are performed on the KDD’99 Data Set which is a standard intrusion detection
benchmark. Compared with other approaches on the same benchmark, DNNL achieves a high detection rate and low false alarm rate. 相似文献
