Concerning the problem of low semantic relevance between nodes and low query efficiency in the current knowledge graph query, an entity-related query method was proposed,and then a knowledge gragh based enterprise query system was designed and implemented base on it. In this method, a four-layer filtering model was adopted. And firstly, the common paths of the target node were found through path search, so that the query nodes with a low degree of relevance were filtered out, and the filtering set was obtained. Then, the relevance degrees of the filtering set's attributes and relationships were calculated in the middle two layers, after that, the graph set filtering was performed based on the dynamic threshold. Finally, the entity relevance and relationship relevance scores was integrated and sorted to obtain the final query result. Experimental results on real enterprise data show that compared with traditional graph query algorithms such as Ness and NeMa, the proposed method reduces the query time by an average of 28.5%, and at the same time increases the filtering performance by an average of 29.6%, verifying that the algorithm can efficiently complete the task of query and display entities associated with the target.

Aiming at the problems of the single result of recommending similar users and insufficient understanding of user interests and behavior information for single social network platforms, a User Recommendation method of Cross-Platform based on Knowledge graph and Restart random walk (URCP-KR) was proposed. First, in the similar subgraphs segmented and matched by the target platform graph and the auxiliary platform graph, an improved multi-layer Recurrent Neural Network (RNN) was used to predict the candidate user entities. And the similar users were selected by comprehensive use of the similarity of topological structure features and user portrait similarity. Then, the relationship information of similar users in the auxiliary platform graph was used to complete the target platform graph. Finally, the probabilities of the users in the target platform graph walking to each user in the community were calculated, so that the interest similarity between users was obtained to realize the user recommendation. Experimental results show that the proposed method has higher recommendation precision and diversity than Collaborative Filtering (CF) algorithm, User Recommendation algorithm based on Cross-Platform online social network (URCP) and User Recommendation algorithm based on Multi-developer Community (UR-MC) with the recommendation precision up to 95.31% and the recommendation coverage up to 88.42%.

A large number of self-made videos on the Internet lack user ratings and the recommendation accuracies of them are not high. In order to solve the problems, a Video Recommendation algorithm based on Danmaku Sentiment Analysis and topic model (VRDSA) was proposed. Firstly, sentiment analysis was performed to video' danmaku comments to obtain the sentiment vectors of the videos, which were used to calculate the emotional similarities between the videos. At the same time, based on the tags of videos, a topic model was built to obtain the topic distribution of the video tags which was used to calculate the topic similarities between the videos. Secondly, the emotional similarities and topic similarities were merged to calculate synthesis similarities between the videos. Thirdly, combined with the comprehensive similarities between the videos and the user's history records, the user preference for videos was obtained. At the same time, the video public recognitions were quantified by user interaction metrics such as the number of likes, danmakus and collections, and the comprehensive recognitions of the videos were calculated by combining the user's history records. Finally, based on the user preference and video comprehensive recognitions, the user's recognitions of videos were predicted, and a personalized recommendation list was generated to complete the video recommendation. Experimental results show that, compared with Danmaku video Recommendation algorithm combing Collaborative Filtering and Topic model (DRCFT) and Unifying LDA (Latent Dirichlet Allocation) and Ratings Collaborative Filtering (ULR-itemCF), the proposed algorithm has the precision increased by 17.1% on average, the recall increased by 22.9% on average, and the F1 increased by 22.2% on average. The proposed algorithm completes the recommendation of videos by analyzing the sentiments of danmakus and integrating the topic model, and fully exploits the emotionality of damaku data to make the recommendation results more accurate.

Concerning the problem that in the research of spatial crowdsourcing task allocation, the benefits of multiple participants and the global optimization of continuous task allocation are not considered, which leads to the problem of poor allocation effect, an online task allocation algorithm was proposed for the global optimization of tripartite comprehensive benefit. Firstly, the distribution of crowdsourcing objects (crowdsourcing tasks and workers) in the next time stamp was predicted based on online random forest and gated recurrent unit network. Then, a bipartite graph model was constructed based on the situation of crowdsourcing objects in the current time stamp. Finally, the optimal matching algorithm of weighted bipartite graph was used to complete the task allocation. The experimental results show that the proposed algorithm realize the global optimization of continuous task allocation. Compared with greedy algorithm, this algorithm improves the success rate of task allocation by 25.7%, the average comprehensive benefit by 32.2% and the average opportunity cost of workers by 37.8%; compared with random threshold algorithm, the algorithm improves the success rate of task allocation by 27.4%, the average comprehensive benefit by 34.7% and the average opportunity cost of workers by 40.2%.

In order to improve the total utility of task allocation in spatiotemporal crowdsourcing dynamic reality, a Dynamic Threshold algorithm based on online Random Forest (DTRF) was proposed. Firstly, the online random forest was initialized based on the historical matching data of workers and tasks on the crowdsourcing platform. Then, the online random forest was used to predict the expected task return rate of each worker as the threshold, and the candidate matching set was selected for each worker according to the threshold. Finally, the matching with the highest sum of current utility was selected from the candidate match set, and the online random forest was updated based on the allocation result. The experiments show that the algorithm can improve the average income of workers while increasing the total utility. Compared with the greedy algorithm, the proposed algorithm has the task assignment rate increased by 4.1%, the total utility increased by 18.2%, and the average worker income increased by 11.2%. Compared with the random threshold algorithm, this algorithm has a better improvement in task allocation rate, total utility, average income of workers with better stability.

Focusing on the poor overall allocation effect due to the total utility of task allocation or task waiting time being considered respectively in the study of task allocation under spatial crowdsourcing environment, a dynamic threshold algorithm based on allocation time factor was proposed. Firstly, the allocation time factor of task was calculated based on the estimated waiting time and the already waiting time. Secondly, the task allocation order was obtained by comprehensively considering the return value of task and the allocation time factor. Thirdly, the dynamic adjustment item was added based on the initial value to set the threshold for each task. Finally, candidate matching set was set for each task according to the threshold condition, and the candidate matching pair with the largest matching coefficient was selected from the candidate matching set to join the result set, and the task allocation was completed. When the task allocation rate was 95.8%, compared with greedy algorithm, the proposed algorithm increased total allocation utility by 20.4%; compared with random threshold algorithm, it increased total allocation utility by 17.8% and decreased task average waiting time by 13.2%; compared with Two phase based Global Online Allocation-Greedy (TGOA-Greedy) algorithm, it increased total allocation utility by 13.9%. The experimental results show that proposed algorithm can shorten the average waiting time of task while improving the total utility of task allocation, to achieve the balance between the total allocation utility and the task waiting time.

Concerning the problem that existing software crowdsourcing worker selection mechanisms do not consider the collaboration among workers, a crowdsourcing worker selection mechanism with bidding model based on active time grouping was proposed. Firstly, crowd-sourced workers were divided into multiple collaborative working groups based on active time. Then, the weights of the working groups were calculated according to the development capabilities of the workers in the group and collaboration factors. Finally, the collaborative working group with the highest weight was selected as the optimal working group, and the most suitable worker from this group was selected for each task module according to the complexity of the module. The experimental results show that the proposed mechanism has a gap of only 0.57% in the average worker ability compared to the ability only allocation method. At the same time, it reduces the project risk by an average of 32% due to the ensurence of the cooperation between workers, which can effectively guide the selection of workers for multi-person collaborative crowdsourcing software tasks.

Aiming at the problem that the existing software crowdsourcing platforms do not consider the order of task release, a method of calculating Task Release Priority (TRP) of software crowdsourcing based on task publisher weight and task weight was proposed. Firstly, a time weight function based on semi-sinusoidal curve was used to measure the activity of the task publisher and the cumulative turnover of the task, so as to calculate the weight of the task publisher. Secondly, the task complexity was calculated according to the system architecture diagram and data flow diagram to measure module complexity, design complexity and data complexity, and the task benefit factor and task emergency factor were calculated based on task quotation and task duration. In this way, the task weight was calculated. Finally, the task publishing priority would be given according to task publisher weight and task weight. The experimental results show that the proposed algorithm not only is effective and reasonable, but also has a maximum success rate of 98%.

The existing software crowdsourcing platforms do not consider the ability of workers adequately, which leads to the low completion quality of tasks assigned to workers. In order to solve the problem, a new Ability Dynamic Measurement algorithm (ADM) for software crowdsourcing workers was proposed to achieve the dynamic measurement of the workers' ability. Firstly, the initial ability of a worker was calculated based on his static skill coverage rate. Secondly, for the single task completed by the worker in history, task complexity, task completion quality, and task development timeliness were integrated to realize the calculation of development ability, and the development ability decaying with time was calculated according to a time factor. Then, according to the time sequence of all the completed tasks in history, the dynamic update of ability measurement value was realized. Finally, the development ability of the worker for a task to be assigned was calculated based on the skill coverage rates of historical tasks. The experimental results show that, compared with the user reliability measurement algorithm, the proposed ability dynamic measurement algorithm has a better rationality and effectiveness, and the average coincidence degree of ability measurement is up to 90.5%, which can effectively guide task assignment.

Since collaborative filtering recommendation algorithm is inefficient in large data environment, a personalized recommendation algorithm based on location bitcode tree, called LB-Tree, was developed. Combined with the characteristics of the MapReduce framework, a novel approach which applyed the index structure in personalized recommendation processing was proposed. For efficient parallel computing in MapReduce, a novel storage strategy based on the differences between clusters was presented. According to the distribution, each cluster was partitioned into several layers by concentric circles with the same centroid, and each layer was expressed by binary bitcodes with different length. To make the frequently recommended data search path shorter and quickly determine the search space by using the index structure, an index tree was constructed by bitcodes of all the layers. Compared with the Top- N recommendation algorithm and Similarity-Based Neighborhood Method (SBNM), LB-Tree has the highest accuracy with the slowest time-increasing, which verifies the effectiveness and efficiency of LB-Tree.

Concerning that the traditional Web text clustering algorithm without considering the Web text topic information leads to a low accuracy rate of multi-topic Web text clustering, a new algorithm was proposed for Web text clustering based on the topic theme. In the method, multi-topic Web text was clustered by three steps: topic extraction, feature extraction and text clustering. Compared to the traditional Web text clustering algorithm, the proposed method fully considered the Web text topic information. The experimental results show that the accuracy rate of the proposed algorithm for multi-topic Web text clustering is higher than the text clustering method based on K-means or HowNet.

To improve the accuracy of recommended Web resources, a personalized recommendation algorithm based on ontology, named BO-RM, was proposed. Subject extraction and similarity measurement methods were designed, and ontology semantic was used to cluster Web resources. With a user's browser tracks captured, the tendency of preferences and recommendation were adjusted dynamically. Comparison experiments with collaborative filtering algorithm based on situation named CFR-RM and personalized prediction algorithm based on model were given. The results show that BO-RM has relatively stable overhead time and good performance in Mean Reciprocal Rank (MRR) and Mean Average Precision (MAP). The results prove that BO-RM improves the efficiency by using offline data analysis for large Web resources, thus it is practical. In addition, BO-RM captures the users' interest in real-time to updates the recommendation list dynamically, which meets the real needs of users.