Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN). Evidently, cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency and disruption of the accessible information among SNs. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. We proposed the unsupervised Friend Relationship-based User Identification algorithm without Prior knowledge (FRUI-P). The FRUI-P first extracts the friend feature of each user into friend feature vector, and then calculates the similarities of all the candidate identical users between two SNs. Finally, a one-to-one map scheme is developed to identify the users based on the similarities. Results of extensive experiments demonstrated that FRUI-P performs much better than current state-of-art network structure-based algorithm without prior knowledge. Due to its high precision, FRUI-P can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users are unobtainable.

With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typical tasks: book rating recommendation, user rating recommendation, and the book summary recommendation; it also explores the feasibility of LLM technology in book recommendation scenarios. At the same time, based on different evaluation schemes for book recommendation tasks and the existing classic recommendation models, this paper discusses the advantages and disadvantages of the BookGPT in book recommendation scenarios and analyzes the opportunities and improvement directions for subsequent LLMs in these scenarios. The experimental research shows the following: (1) The BookGPT can achieve good recommendation results in existing classic book recommendation tasks. Especially in cases containing less information about the target object to be recommended, such as zero-shot or one-shot learning tasks, the performance of the BookGPT is close to or even better than that of the current classic book recommendation algorithms, and this method has great potential for improvement. (2) In text generation tasks such as book summary recommendation, the recommendation effect of the BookGPT model is better than that of the manual editing process of Douban Reading, and it can even perform personalized interpretable content recommendations based on readers’ attribute and identity information, making it more persuasive than interpretable one-size-fits-all recommendation models. Finally, we have open-sourced the relevant datasets and experimental codes, hoping that the exploratory program proposed in this paper can inspire the development of more LLMs to expand their applications and theoretical research prospects in the field of book recommendation and general recommendation tasks.

In the research of social networks, their structural characteristics, user social behaviors, and user relationships are elements that are important to understand social networks, to predict user behaviors, and to manage social networks. In this article, we took as the research object of social networks the mobile communication network, which is closely connected with people's real lives. We studied the structural characteristics using the complex network analysis methods and derived the laws that exist in the structure of communication social networks. We analyzed users' social behaviors or social patterns from the perspective of age, gender, social scope, age differences, and time. In addition, we extracted various salient features of user's calling behavior, and used the XGBoost and logistic regression (LR) fusion method to establish the Kinship-XL model, which is able to improve the performance and speed. Through the experimental verification, the Kinship-XL model can determine whether there is a kinship between users or not.

Oracle bone inscriptions are the earliest mature writing system discovered in China. Oracle is usually published in the form of glyphs and pictures, and the related oracle font database environment is closed and limited. Due to the lack of standardization of ancient oracle bone inscriptions, the structure and glyph of the same characters are not unified, and a large number of different shapes and complicated characters coexist. Even ancient character devotees need to complete the basic recognition of oracle with the help of professional reference books and experts. In addition, in the field of traditional literature, distinguishing variant characters of oracle-bone inscriptions needs strong expert knowledge and lacks efficiency. The recognition process consists of two stages. According to the characteristics of oracle-bone inscriptions, such as left-right symmetry, up-down symmetry and positive and negative coexistence, the corresponding data enhancement methods are used to get sufficient training samples in the first stage. In the second stage, the recognition candidate set under each threshold condition in the first stage are used to select the recognition candidate set which is empty under the specific threshold condition, and introduce the prior knowledge to distinguish the oracle variants. In the first stage, computer related methods are used to identify oracle variants. In the second stage, on the basis of the first stage, a set of recognition results that cannot be determined roughly is selected, and a priori knowledge is introduced to integrate multi-domain methods to identify oracle variants. The method proposed in this paper has achieved great results in the recognition of oracle variant characters.

A least squares support vector machine (LS-SVM) offers performance comparable to that of SVMs for classification and regression. The main limitation of LS-SVM is that it lacks sparsity compared with SVMs, making LS-SVM unsuitable for handling large-scale data due to computation and memory costs. To obtain sparse LS-SVM, several pruning methods based on an iterative strategy were recently proposed but did not consider the quantity constraint on the number of reserved support vectors, as widely used in real-life applications. In this article, a noniterative algorithm is proposed based on the selection of globally representative points (global-representation-based sparse least squares support vector machine, GRS-LSSVM) to improve the performance of sparse LS-SVM. For the first time, we present a model of sparse LS-SVM with a quantity constraint. In solving the optimal solution of the model, we find that using globally representative points to construct the reserved support vector set produces a better solution than other methods. We design an indicator based on point density and point dispersion to evaluate the global representation of points in feature space. Using the indicator, the top globally representative points are selected in one step from all points to construct the reserved support vector set of sparse LS-SVM. After obtaining the set, the decision hyperplane of sparse LS-SVM is directly computed using an algebraic formula. This algorithm only consumes O(N2) in computational complexity and O(N) in memory cost which makes it suitable for large-scale data sets. The experimental results show that the proposed algorithm has higher sparsity, greater stability, and lower computational complexity than the traditional iterative algorithms.