Users’ social media contains their past personal experiences and potential life patterns， and the study of their patterns is of great value for predicting users’ future behaviors and performing personalized recommendations for users. By collecting Weibo data， 11 types of events were defined， and a three?stage Pipeline system was proposed to detect personal events by using BERT （Bidirectional Encoder Representations from Transformers） pre?trained models in three stages respectively， including BERT+BiLSTM+Attention， BERT+FullConnect and BERT+BiLSTM+CRF. The information of whether the text contained defined events， the event types of events contained， and the elements contained in each event were extracted from the Weibo， and the specific elements are Subject （subject of the event）， Object （event element）， Time （event occurrence time）， Place （place where the event occurred） and Tense （tense of the event）， thereby exploring the change law of user’s personal event timeline to predict personal events. Comparative experiments and analysis were conducted with classification algorithms such as logistic regression， naive Bayes， random forest and decision tree on a collected real user Weibo dataset. Experimental results show that the BERT+BiLSTM+Attention， BERT+FullConnect， BERT+BiLSTM+CRF methods used in three stages achieve the highest F1?score， verifying the effectiveness of the proposed methods. Finally， the personal event timeline was visually built according to the extracted events with time information.
In intelligent computing services， data analysis and processing are provided for the service consumer by the service provider through Internet， and a learning model is established to complete intelligent computing function. Due to the lack of effective communication channels between service providers and service consumers， as well as the fuzzy and messy requirement descriptions of the service consumer feedback， there is a lack of a unified service requirement acquisition method to effectively analyze， organize and regulate the continuously changing requirement of users， which leads to the failure of intelligent computing services to make a rapid improvement according to the user’s requirements. Aiming at the problems of continuity and uncertainty of requirement changes in service development， a requirement acquisition method for intelligent computing services was proposed. The application feedback and questions of intelligent computing services were firstly obtained from Stack Overflow question and answer forum. Then， the knowledge classification and prioritization were performed on them by using different learning models （including Support Vector Machine （SVM）， naive Bayes and TextCNN） according to the types of requirements concerned by the service consumer. Finally， a customized service requirement template was used to describe the requirements of intelligent computing services.
Existing product and service quality analysis is often based on questionnaire survey or product reviews， but there are problems such as difficulty in questionnaire collection and invalid data in product reviews. As a bridge between customers and businesses， the customer service dialogue contains rich customer opinions from product to service perspective， however， there are still few studies using customer service dialogues to analyze product and service quality. A product and service quality analysis method based on customer service dialogues was proposed， which firstly combined the product features and service blueprint to determine product and service quality evaluation factors， and used the Important?Performance Analysis （IPA） method to define the importance and performance index of evaluation factors. Then， quantitative analysis of the importance and satisfaction of products and services was performed by using the dialogue topic extraction and sentiment analysis. The method was applied on the real customer service dialogues of a Taobao flagship store which sells disinfection and sterilization products， and 18 evaluation factors were established， whose importance and performance were quantified based on more than 900 thousand real historical customer service dialogues， thereby analyzing the quality of products and services of the flagship store. Finally， a questionnaire on the professional customer service employees was carried out to verify the effectiveness of the proposed method.
Mobile Edge Computing （MEC） can reduce the energy consumption of mobile devices and the delay of users’ acquisition to services by deploying resources in users’ neighborhood； however， most relevant caching studies ignore the regional differences of the services requested by users. A cache cooperation strategy for maximizing revenue was proposed by considering the features of requested content in different regions and the dynamic characteristic of content. Firstly， considering the regional features of user preferences， the base stations were partitioned into several collaborative domains， and the base stations in each collaboration domain was able to serve users with the same preferences. Then， the content popularity in each region was predicted by the Auto?Regressive Integrated Moving Average （ARIMA） model and the similarity of the content. Finally， the cache cooperation problem was transformed into a revenue maximization problem， and the greedy algorithm was used to solve the content placement and replacement problems according to the revenue obtained by content storage. Simulation results showed that compared with the Grouping?based and Hierarchical Collaborative Caching （GHCC） algorithm based on MEC， the proposed algorithm improved the cache hit rate by 28% with lower average transmission delay. It can be seen that the proposed algorithm can effectively improve the cache hit rate and reduce the average transmission delay at the same time.
Current research on Application Program Interface （API） learning and code reuse focuses on mining frequent API usage patterns， extracting component information， and recommending personalized API services based on user requirements and target functions. However， as beginners in software development who lack professional knowledge， experience and skills to implement specific use cases， they often need real code use cases as a reference except reading official documents. Most of the existing research about code recommendation is in single fragment mode. The lack of cross function case in case selection is not conducive for beginners to learn to build a complete use scenario or a functional module. At the same time， the semantic description extracted from a single function annotation is not enough for learners to understand the complete function implementation method of the project. To solve the above problems， an API use case recommendation service based on open source community analysis was proposed. Taking the software development back?end framework Spring Boot as an example， a cross function case recommendation assistant learning service was constructed. Then， the feasibility and effectiveness of the proposed API use case recommendation service was verified through questionnaires and expert verification.
The current service resources in Internet of Services （IoS） show a trend of refinement and specialization. Services with single function cannot meet the complex and changeable requirements of users. Service integrating and scheduling methods have become hot spots in the field of service computing. However， most existing service integrating and scheduling methods only consider the satisfaction of user requirements and do not consider the sustainability of the IoS ecosystem. In response to the above problems， a service integration method based on adaptive multi?objective reinforcement learning was proposed. In this method， a multi?objective optimization strategy was introduced into the framework of Asynchronous Advantage Actor?Critic （A3C） algorithm， so as to ensure the healthy development of the IoS ecosystem while satisfying user needs. The integrated weight of the multi?objective value was able to adjusted dynamically according to the regret value， which improved the imbalance of sub?objective values in multi?objective reinforcement learning. The service integration verification was carried out in a real large?scale service environment. Experimental results show that the proposed method is faster than traditional machine learning methods in large?scale service environment， and has a more balanced solution quality of each objective compared with Reinforcement Learning （RL） with fixed weights.
Mainstream personalized recommendation systems usually use models deployed in the cloud to perform recommendation， so the private data such as user interaction behaviors need to be uploaded to the cloud， which may cause potential risks of user privacy leakage. In order to protect user privacy， user-sensitive data can be processed on the client， however， there are communication bottleneck and computation resource bottleneck in clients. Aiming at the above challenges， a personalized recommendation service system based on cloud-client-convergence was proposed. In this system， the cloud-based recommendation model was divided into a user representation model and a sorting model. After being pre-trained on the cloud， the user representation model was deployed to the client， while the sorting model was deployed to the cloud. A small-scale Recurrent Neural Network （RNN） was used to model the user behavior characteristics by extracting temporal information from user interaction logs， and the Lasso （Least absolute shrinkage and selection operator） algorithm was used to compress user representations， thereby preventing a drop in recommendation accuracy while reducing the communication overhead between the cloud and the client as well as the computation overhead of the client. Experiments were conducted on RecSys Challenge 2015 dataset， and the results show that the recommendation accuracy of the proposed system is comparable to that of the GRU4REC model， while the volume of the compressed user representations is only 34.8% of that before compression， with a lower computational overhead.
In view of the general problems of not fully utilizing historical information and slow parameter optimization process in the research of clustering algorithms， an adaptive classification algorithm based on data field was proposed in combination with edge intelligent computing， which can be deployed on Edge Computing （EC） nodes to provide local intelligent classification service. By introducing supervision information to modify the structure of the traditional data field clustering model， the proposed algorithm enabled the traditional data field to be applied to classification problems， extending the applicable fields of data field theory. Based on the idea of the data field， the proposed algorithm transformed the domain value space of the data into the data potential field space， and divided the data into several unlabeled cluster results according to the spatial potential value. After comparing the cluster results with the historical supervision information for cloud similarity， the cluster results were attributed to the most similar category. Besides， a parameter search strategy based on sliding step length was proposed to speeded up the parameter optimization of the proposed algorithm. Based on this algorithm， a distributed data processing scheme was proposed. Through the cooperation of cloud center and edge devices， classification tasks were cut and distributed to different levels of nodes to achieve modularity and low coupling. Simulation results show that the precision and recall of the proposed algorithm maintained above 96%， and the Hamming loss was less than 0.022. Experimental results show that the proposed algorithm can accurately classify and accelerate the speed of parameter optimization， and outperforms than Logistic Regression （LR） algorithm and Random Forest （RF） algorithm in overall performance.
Microservice invocation link data is a type of important data generated in the daily operation of the microservice application system， which records a series of service invocation information corresponding to a user request in the microservice application in the form of link. Microservice invocation link data are generated at different microservice deployment nodes due to the distribution characteristic of the system， and the current collection methods for these distributed data include full collection and sampling collection. Full collection may bring large data transmission and data storage costs， while sampling collection may miss critical invocation data. Therefore， an event?driven and pipeline sampling based dynamic collection method for microservice invocation link data was proposed， and a microservice invocation link system that supports dynamic collection of invocation link data was designed and implemented based on the open?source software Zipkin. Firstly， the pipeline sampling was performed on the link data of different nodes that met the predefined event features， that is the same link data of all nodes were collected by the data collection server only when the event defined data was generated by a node； meanwhile， to address the problem of inconsistent data generation rates of different nodes， multi?threaded streaming data processing technology based on time window and data synchronization technology were used to realize the data collection and transmission of different nodes. Finally， considering the problem that the link data of each node arrives at the server in different sequential order， the synchronization and summary of the full link data were realized through the timing alignment method. Experimental results on the public microservice lrevocation ink dataset prove that compared to the full collection and sampling collection methods， the proposed method has higher accuracy and more efficient collection on link data containing specific events such as anomalies and slow responces.
