In the open social networks, the analysis of user data after the injection attack has a great impact on the recommendation system. K-Nearest Neighborbased collaborative filtering algorithms are very vulnerable to this attack. Another recommendation algorithm based on probabilistic latent semantic analysis has relatively accurate recommendation, but it is not very stable and robust against attacks on the overall user data of the recommendation system. In this paper, we propose to use to DeepWalk the user network processing, while taking advantage of the user profile feature time series to consider the user's behavior over time, the algorithm also analyzes the stability and robustness of DeepWalk and user profile. The results show that especially the DeepWalk-based approach can achieve comparable recommendation accuracy.
Bioinformatics applications which are both dataintensive and computation-intensive bring great challenges to their development and optimization. In order to study and accelerate bioinformatics data analysis models, a method named data transformation graph (DTG) is introduced first. It describes scientific data analysis models by dependencies and transformations among their data items. Then, taking BLAST as an example, DTG is used to study the data dependency in this popular bioinformatics data analysis model and parallel it by both query splitting and database partition. At last, parallel versions of BLAST proposed by DTG are implemented based on a distributed data-intensive computing middleware called Robinia. The result of performance test shows that parallel BLAST can achieve near-linear speedup with good scalability, and data transformation graph can be used to study, parallelize and optimize bioinformatics analysis applications for higher performance.
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