To solve the problem of uncertain Quality of Service (QoS)-aware cloud service composition optimization, an Uncertain-Long Time Series (ULST) model and Tournament strategy based Genetic Algorithm (T-GA) was proposed. Firstly, based on different access rules of users to services in different periods, the long-term change of QoS was modeled as an uncertain-long time series, which can accurately describe the users' actual QoS access record to service over a period of time. Secondly, an improved genetic algorithm based on uncertain QoS model was proposed, which used tournament strategy instead of basic roulette wheel selection strategy. Finally, a lot of experiments were carried out on real data. The uncertain-long time series model can effectively solve the problem of uncertain QoS-aware cloud service composition; the proposed T-GA is superior to the Genetic Algorithm based on Elite selection strategy (E-GA) in optimization results and stability, and the execution speed is improved by almost one time, which is a feasible, high efficient and stable algorithm.

Concerning the problems of current social networking friends recommended methods, such as no obvious user interest and poor correlation between the users, a collaborative filtering algorithm was proposed based on common users and similar labels. The common concerned users were extracted as joint project data, and the custom labels were added to reflect the users' interest. Then the semantic similarity of the labels was calculated to expand the sparse matrix and improve the collaborative filtering recommendation. The experimental results show that, compared with the traditional collaborative filtering algorithm with single index, the proposed algorithm can better reflect the users' interest, and has significant improvement in the recommended accuracy and the average accuracy.