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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Personalized friends recommendation is animportant wayto promote the service quality of social networks. In alarge-scale social network, accurately recommending friends with similar themes can make usersstickier，but the sparseness of the massive data makes most of the existing social networks cannot accurately make friend recommendations based on similarities in the interests of users. To address this issue，a personalized topic-oriented recommendation method based on user's interest (ITOR) is proposed. Firstly, the topics of user's interests are extracted by K-core analyzing method. Secondly，combining the users? attributes information，the probability of being good friends can be computed. The satisfaction and accuracy of recommendation resultsare further enhanced with this probability. Finally，we verify the effectiveness of the proposed method by crawling SinaWeibo data released in September 2015. [ABSTRACT FROM AUTHOR]

The global field experiment network is rapidly growing in ecological research in recent years. Some specific methods have emerged, such as the Coordinated Distributed Experiments (CDE), Distributed Collaborative Experiments (DCE), and field observational network of British Biological Records Centre (BRC). However, problems including too small scale, short duration and biased data are criticized in these methods. Construction of the protocol of field experiment network should follow several principles: controlled experiment prior to observation, quantity prior to quality of data, and scale prior to operation. Here, I advocated the application of citizen science to the obtaining of the data in large field, at multi-scale, and with a long duration. Environmental factors could be considered as covariant to test the dataset provided by citizen participants. Furthermore, the same dataset, as posterior probability, could be compared with the priori data set provided by ecologists to test the validity of data. This methodology, with the corresponding statistical model, would overcome the shortcoming of qualitative bias of data in citizen science. The application of priori probability, logistical relation between priori and posteriori probability, and possibility of discovering new causality of evolutionary process in ecological experimental data were discussed. Compared with CDE, CED, and BRC, this method improved the match between statistical norm and sampling quantity in large spatial and temporal scales. This new method would help discover the general theory of ecology researches and it could be termed "Coordinated Distributed Experiments 2.0" (CDE 2.0).