In reality， recommender systems usually suffer from various bias problems， such as exposure bias， position bias and selection bias. A recommendation model that ignores the bias problems cannot reflect the real performance of the recommender system， and may be untrustworthy for users. Previous works show that a recommendation model based on propensity score estimation can effectively alleviate the exposure bias problem of implicit feedback data in recommender systems， but only item information is usually considered to estimate propensity scores， which may lead to inaccurate estimation of propensity scores. To improve the accuracy of propensity score estimation， a Match Propensity Estimator （MPE） method was proposed. Specifically， a concept of users’ popularity preference was introduced at first， and then more accurate modeling of the sample exposure rate was achieved by calculating the matching degree of the user’s popularity preference and the item’s popularity. The proposed estimation method was integrated with a traditional recommendation model and an unbiased recommendation model， and the integrated models were compared to three baseline models including the above two models. Experimental results on a public dataset show that the models combining MPE method achieve significant improvement on three evaluation metrics such as recall， Discounted Cumulative Gain （DCG） and Mean Average Precision （MAP） compared with the corresponding baseline models respectively. In addition， experimental results demonstrate that a large part of the performance gain comes from long-tail items， showing that the proposed method is helpful to improve the diversity and coverage of recommended items.

In recommender system field， most of the existing works mainly focus on the One-Class Collaborative Filtering （OCCF） problem with only one type of users’ feedback， e.g.， purchasing feedback. However， users’ feedback is usually heterogeneous in real applications， so it has become a new challenge to model the users’ heterogeneous feedback to capture their true preferences. Focusing on the Heterogeneous One-Class Collaborative Filtering （HOCCF） problem （including users’ purchasing feedback and browsing feedback）， a transfer learning solution named Staged Variational AutoEncoder （SVAE） model was proposed. Firstly， the latent feature vectors were generated via the Multinomial Variational AutoEncoder （Multi-VAE） with users’ browsing feedback auxiliary data. Then， the obtained latent feature vectors were transferred to another Multi-VAE to assist the modeling of users’ target data， i.e.， purchasing feedback by this Multi-VAE. Experimental results on three real-world datasets show that the performance of SVAE model on the important metrics such as Precision@5 and Normalized Discounted Cumulative Gain@5 （NDCG@5） is significantly better than the performance of the state-of-the-art recommendation algorithms in most cases， demonstrating the effectiveness of the proposed model.

Concerning the limited influence scope and high time complexity of existing influence maximization algorithms in social networks, a k-core filtered algorithm based on independent cascade model was proposed. Firstly, an existing influence maximization algorithm was introduced, its rank of nodes does not depend on the entire network. Secondly, pre-training was carried out to find the value of k which has the best optimization effect on existing algorithms but has no relation with the number of selected seeds. Finally, the nodes and edges that do not belong to the k-core subgraph were filtered by computing the k-core of the graph, then the existing influence maximization algorithms were applied on the k-core subgraph, thus reducing computational complexity. Several experiments were conducted on datasets with different scale to prove that the k-core filtered algorithm has different optimization effects on different influence maximization algorithms. After combined with k-core filtered algorithm, compared with the original Prefix excluding Maximum Influence Arborescence (PMIA) algorithm, the influence range is increased by 13.89% and the execution time is reduced by as much as 8.34%; compared with the original Core Covering Algorithm (CCA), the influence range has no obvious difference and the execution time is reduced by as much as 28.5%; compared with the original OutDegree algorithm, the influence range is increased by 21.81% and the execution time is reduced by as much as 26.96%; compared with the original Random algorithm, the influence range is increased by 71.99% and the execution time is reduced by as much as 24.21%. Furthermore, a new influence maximization algorithm named GIMS (General Influence Maximization in Social network) was proposed. Compared with PIMA and Influence Rank Influence Estimation (IRIE), it has wider influence range while still keeping execution time at second level. When it was combined with k-core filtered algorithm, the influence range and execution time do not have significant change. The experimiental results show that k-core filtered algorithm can effectively increase the influence ranges of existing algorithms and reduce their execution times; in addition, the proposed GIMS algorithm has wider influence range and better efficiency, and it is more robust.

Focused on the problem of low recognition in recognition systems caused by the inaccuracy of input features, an efficient Deep Sparse Auto-Encoder (DSAE) method using Extreme Learning Machine (ELM) for facial features was proposed. Firstly, truncated nuclear norm was used to construct loss function, and sparse features of face images were extracted by minimizing loss function. Secondly, self-encoding of facial features was used by Extreme Learning Machine Auto-Encoder (ELM-AE) model to achieve data dimension reduction and noise filtering. Thirdly, the optimal depth structure was obtained by minimizing the empirical risk. The experimental results on ORL, IMM, Yale and UMIST datasets show that the DSAE method not only has higher recognition rate than ELM, Random Forest (RF), etc. on high-dimensional face images, but also has good generalization performance.

Concerning of the low accurate rate of active defense technology, a heuristic detection system of Trojan based on the analysis of trajectory was proposed. Two kinds of typical Trojan trajectories were presented, and by using the behavioral data on Trojan trajectory the danger level of the suspicious file was detected with the decision rules and algorithm. The experimental results show that the performance of detecting unknown Trojan of this system is better than that of the traditional method, and some special Trojans can also be detected.

Interactive image segmentation methods usually ask users to mark much more initial seeds or more than one interaction when they are used for medical image segmentation with fuzzy boundaries. This paper presented an optimized interactive image segmentation algorithm with multi-threshold technology. Based on GorwCut algorithm put forward by Vladimir, the optimized algorithm introduced image gray histogram with more than one threshold values to generate initial seeds template automatically and then used improved cellular automaton iterative algorithm to realize image segmentation. The algorithm simplified the user interactive operations and improved the segmentation accuracy. In applications, the algorithm was used to test on 100 plaque and liver image segmentations respectively, of which the results show that the optimized algorithm is of good performance.