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It is always challenging task for students to select right universities. For students, graduate job placement is the most important component of university quality. However, existing university evaluation methods predominantly depend on either subjective criteria, such as the perceived quality of the learning environment and academic prestige, or on factors like faculty excellence, which may not provide a precise indication of graduate job placement. Indeed, there is still a lack of a data-driven approach to accurately measure university quality based on the employment situation of graduates. Moreover, the inherently unsupervised nature of university evaluation, compounded by the absence of a reasonable ground truth, necessitates the development of a reliable supervised methodology to precisely quantify university quality. Our basic assumption is that highly influential companies would attract graduates from high-ranking universities. To this end, in this paper, we formulate university evaluation problem into the graduate flow prediction problem, and propose an Influence based Graph Autoencoder (IGAE) method to learn the representation of universities based on the employment of their graduates. Specifically, we first build a talent transition graph based on the massive resume information. This graph reveals the flow of talent between institutions. Then, considering the asymmetric and heterogeneous properties of talent flow, an unidirectional aggregation process with a heterogeneous attention mechanism is designed to encode the nodes in the directed graph and preserve the influence terms at the same time. Afterwards, a novel dual self-attention module is exploited to capture the dynamic pattern of institutions to forecast future employment. Furthermore, we design an influence based decoder to predict the existence of talent flows and estimate the frequency of employment, which can be learnt in a joint learning framework. Finally, we conduct extensive experiments on a real-world dataset for performance evaluation. The experimental results clearly validate the effectiveness of our approach compared to the state-of-the-art baselines, and we provide a case study on university influence analysis.

Recent years have witnessed the rapid development of online recruitment platforms, which provide a convenient way for matching job seekers and recruiters. Indeed, this is a reciprocal recommendation problem which needs to consider the preferences of both job seekers and recruiters simultaneously. Existing studies mainly focus on building recommendation models based on text matching or collaborative filtering methods. However, we propose that these methods are limited and insufficient, since the abundant multi-typed bilateral behaviors among users have been largely ignored. Therefore, in this paper, we propose a novel BilAteral Multi-BehaviOr mOdeling (BAMBOO) method for reciprocal recommendation in online recruitment, which can model the multi-typed interactions from expectation perspective and competitiveness perspective. Specifically, for the expectation perspective, we propose to format the historical behaviors of different users as bilateral multi-behavior sequences, and we utilize a transformer-based model to learn the representations of what the users want to obtain. For the competitiveness perspective, we propose to construct a bilateral interaction heterogeneous graph to describe the entire recruitment market, and further utilize a heterogeneous graph transformer-based model to learn the representations of what the users can obtain. Moreover, we utilize contrastive learning methods to enhance these two modules. Furthermore, we propose to decompose the matching probability into the product of two parts, and we train our model based on a multi-task learning strategy. We conduct both offline experiments on real-world datasets and online A/B test, and the experiment results validate the effectiveness of our BAMBOO model compared with several state-of-the-art baseline methods.

Unsupervised domain adaptation aims to transfer rich knowledge from the annotated source domain to the unlabeled target domain with the same label space. One prevalent genre is the bi-discriminator domain adversarial network, which concurrently considers the decision boundaries of both domains during the domain alignment. While effective, methods within this genre still contend with accuracy agnosticism toward the target domain and overconfident estimation in the source domain. Consequently, these two limitations hinder the effectiveness of both decision boundaries. To address the aforementioned challenges, we propose a novel bi-discriminator domain adversarial neural network, denoted as BACG. Specifically, for accuracy awareness of the target domain, we initially devise an optimizable nearest neighbor algorithm for acquiring pseudo-labels of samples, followed by class-level gradient alignment between two domains. This approach explicitly incorporates the accuracy signal from the target domain. To alleviate overconfident estimation in the source domain, we adopt evidential learning theory and develop a multinomial Dirichlet hierarchical model to infer both the classification probability and uncertainty. This approach not only ensures optimal assumptions for the source domain but also guarantees high-quality domain alignment. Additionally, to lower the time overhead caused by pseudo-label assignment, we introduce a memory bank-based variant, namely, fast-BACG, which effectively accelerates the training process at the expense of a slight reduction in accuracy. Extensive experiments and thorough analysis on four benchmark data sets validate the effectiveness and robustness of our algorithm.

The task of image captioning aims to generate captions directly from images via the automatically learned cross-modal generator. To build a well-performing generator, existing approaches usually need a large number of described images (i.e., supervised image-sentence pairs), requiring a huge effects on manual labeling. However, in real-world applications, a more general scenario is that we only have limited amount of described images and a large number of undescribed images. Therefore, a resulting challenge is how to effectively combine the undescribed images into the learning of cross-modal generator (i.e., semisupervised image captioning). To solve this problem, we propose a novel image captioning method by exploiting the cross-modal prediction and relation consistency (CPRC), which aims to utilize the raw image input to constrain the generated sentence in the semantic space. In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty while using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) prediction consistency: CPRC utilizes the prediction of raw image as soft label to distill useful supervision for the generated sentence, rather than employing the traditional pseudo labeling and 2) relation consistency: CPRC develops a novel relation consistency between augmented images and corresponding generated sentences to retain the important relational knowledge. In result, CPRC supervises the generated sentence from both the informativeness and representativeness perspectives, and can reasonably use the undescribed images to learn a more effective generator under the semisupervised scenario. The experiments show that our method outperforms state-of-the-art comparison methods on the MS-COCO “Karpathy” offline test split under complex nonparallel scenarios, for example, CPRC achieves at least 6% improvements on the CIDEr-D score.

Cognitive diagnosis has been widely recognized as a crucial task in the field of computational education, which is capable of learning the knowledge profiles of students and predicting their future exercise performance. Indeed, considerable research efforts have been made in this direction over the past decades. However, most of the existing studies only focus on individual-level diagnostic modeling, while the group-level cognitive diagnosis still lacks an in-depth exploration, which is more compatible with realistic collaborative learning environments. To this end, in this paper, we propose a Relation-guided Dual-side Graph Transformer (RDGT) model for achieving effective group-level cognitive diagnosis. Specifically, we first construct the dual-side relation graphs (i.e., student-side and exercise-side) from the group-student-exercise heterogeneous interaction data for explicitly modeling associations between students and exercises, respectively. In particular, the edge weight between two nodes is defined based on the similarity of corresponding student-exercise interactions. Then, we introduce two relation-guided graph transformers to learn the representations of students and exercises by integrating the whole graph information, including both nodes and edge weights. Meanwhile, the inter-group information has been incorporated into the student-side relation graph to further enhance the representations of students. Along this line, we design a cognitive diagnosis module for learning the groups’ proficiency in specific knowledge concepts, which includes an attention-based aggregation strategy to obtain the final group representation and a hybrid loss for optimizing the performance prediction of both group and student. Finally, extensive experiments on 5 real-world datasets clearly demonstrate the effectiveness of our model as well as some interesting findings (e.g., the representative groups and potential collaborations among students).

Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weather prediction, most of them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On one hand, the accurate prediction of one task can help improve another task's performance. On the other hand, geospatially distributed air quality and weather monitoring stations provide additional hints for city-wide spatiotemporal dependency modeling. Inspired by the above two insights, in this paper, we propose a multi-view multi-adversarial approach, entitled MasterGNN$^{+}$+, to jointly predict air quality and weather conditions. First, we devise a multi-view graph learning block to model spatial autocorrelation based on geographical distance and environmental context. Then, a dedicated evolved recurrent network is proposed to dynamically capture the long-range and independent temporal autocorrelation for each monitoring station and time slot. After that, we develop a multi-adversarial graph learning framework to against observation noise propagation introduced by spatiotemporal modeling. Moreover, we present an adaptive training strategy by formulating multi-adversarial learning as a multi-task learning problem. Finally, extensive experiments on two real-world datasets show that MasterGNN$^{+}$+ achieves the best performance compared with seven baselines on both air quality and weather prediction tasks.

A job description shows the responsibilities of the job position and the skill requirements for the job. An effective job description will help employers to identify the right talents for the job, and give a clear understanding to candidates of what their duties and qualifications for a particular position would be. However, due to the variation in experiences, it is always a challenge for both hiring managers and recruiters to decide what capabilities the job requires and prioritize them accordingly on the job description. Also, tedious and expensive human efforts are usually required to prepare a job description. Therefore, in this paper, we investigate how to automate the process to generate job descriptions with less human intervention. To this end, we propose an end-to-end capability-aware neural job description generation framework, namely Cajon, to facilitate the writing of job description. Specifically, we first propose a novel capability-aware neural topic model to distill the various capability information from the larger-scale recruitment data. Also, an encoder-decoder recurrent neural network is designed for enabling the job description generation. In particular, the capability-aware attention mechanism and copy mechanism are proposed to guide the generation process to ensure the generated job descriptions can comprehensively cover relevant and representative capability requirements for the job. Moreover, we propose a capability-aware policy gradient training algorithm to further enhance the rationality of the generated job description. Finally, extensive experiments on real-world recruitment data clearly show our Cajon framework can help to generate more effective job descriptions in an interpretable way. In particular, our Cajon framework has been deployed in Baidu as an intelligent tool for talent recruitment.

Every organization has organizational networks for exchange of ideas and information. It is believed that organizational network analysis (ONA) can help the business be more effective. While considerable research efforts have been made for visualizing and analyzing relationships in organizational networks, it lacks a holistic way to model the complex social structures and rich semantic information of these networks. Indeed, employee behaviors can occur across different communication platforms, such as email and instant messaging systems, which naturally lead to the multiplex structure of organizational social networks. Meanwhile, it is also a challenge to model the impact of semantic information, such as employee attributes and organization charts, and the collaboration relationships of employees. To this end, in this paper, we propose a Multiplex Attentive Network Embedding (MANE) approach for modeling organizational social networks in a holistic way. Specifically, we first develop a multiple attributed random walk approach to jointly model multiple networks, with the integration of external work information. Then, we preserve the network structure by maximizing the probability of predicting the central node based on the surrounding context nodes. In particular, we introduce an attention mechanism to assign a weight to each context node in the training process, according to its attributed relation and structural relation with the central node by utilizing the k-core algorithm and the shortest path algorithm. In this way, the embedding results can be kept consistent with their structural relationships. Furthermore, to solve some department-level tasks, we introduce an attentive relational transition method to learn the representation of departments in the organizational networks. Finally, we evaluate the performance of MANE with extensive experiments on real-world data for three important talent management tasks, namely employee performance prediction, employee turnover prediction and department performance prediction. We also conduct a link prediction task to validate the effectiveness of employee embedding. Experimental results clearly show the effectiveness and interpretability of MANE for organizational network analysis.

Nowadays, Knowledge graphs (KGs) have been playing a pivotal role in AI-related applications. Despite the large sizes, existing KGs are far from complete and comprehensive. In order to continuously enrich KGs, automatic knowledge construction and update mechanisms are usually utilized, which inevitably bring in plenty of noise. However, most existing knowledge graph embedding (KGE) methods assume that all the triple facts in KGs are correct, and project both entities and relations into a low-dimensional space without considering noise and knowledge conflicts. This will lead to low-quality and unreliable representations of KGs. To this end, in this paper, we propose a general multi-task reinforcement learning framework, which can greatly alleviate the noisy data problem. In our framework, we exploit reinforcement learning for choosing high-quality knowledge triples while filtering out the noisy ones. Also, in order to take full advantage of the correlations among semantically similar relations, the triple selection processes of similar relations are trained in a collective way with multi-task learning. Moreover, we extend popular KGE models TransE, DistMult, ConvE and RotatE with the proposed framework. Finally, the experimental validation shows that our approach is able to enhance existing KGE models and can provide more robust representations of KGs in noisy scenarios.

In recent decades, innovation and entrepreneurship have become buzz words. With entrepreneurial projects emerging in large numbers every day, the common intention of all investors, i.e., putting every penny into good entrepreneurial projects, is becoming more difficult. In reality, traditional research with empirical results is not practical for analyzing these newly launched projects of small and micro enterprises before production and sale. Actually, the future market prospect is an important criterion for evaluating entrepreneurial projects. However, this direction has not been well explored due to the limitations of scenarios and technical challenges especially for these small and micro enterprises. In this paper, we construct an interesting study of exploiting the market prospect from the sales markets (i.e., E-commerce) to help evaluate newly-posted campaigns in crowdfunding. Specifically, we propose a novel Market-oriented Prospect Analysis with Transferring Attention (MoPa-A) model which contains two learning modules, i.e., HostTask Learning and GuestTask Learning connected and enhanced by transferring attention. The former is designed for funding performance modeling with heterogeneous features of crowdfunding campaigns, and the latter is to represent and transfer the latent semantics of market prospect for target campaigns from campaigns’ comments with the help of relevant products in sales market. The model design of MoPa-A brings some new insights on flexible knowledge transfer for different or cross domains. With the real-world datasets collected from Indiegogo and Amazon, we construct extensive experiments and the results demonstrate the effectiveness of our MoPa-A model.

Corporate relative valuation (CRV) refers to the process of comparing a company’s value from company products, core staff and other related information, so that we can assess the company’s market value, which is critical for venture capital firms. Traditional relative valuation methods heavily rely on tedious and expensive human efforts, especially for non-publicly listed companies. However, the availability of information about company’s invisible assets, such as patents, talent, and investors, enables a new paradigm to learn and evaluate corporate relative values automatically. Indeed, in this paper, we reveal that, the companies and their core members can natually be formed as a heterogeneous graph and the attributes of different nodes include semantically-rich multi-modal data, thereby we are able to extract a latent embedding for each company. The network embeddings can reflect domain experts’ behavior and are effective for corporate relative valuation. Along this line, we develop a heterogeneous multi-modal graph neural network method, named HM$^{2}$2, which deals with embedding challenges involving modal attribute encoding, multi-modal aggregation, and valuation prediction modules. Specifically, HM$^{2}$2 first performs the representation learning for heterogeneous neighbors of the input company by taking relationships among nodes into consideration, which aggregates node attributes via linkage-aware multi-head attention mechanism, rather than multi-instance based methods. Then, HM$^{2}$2 adopts the self-attention network to aggregate different modal embeddings for final prediction, and employs dynamic triplet loss with embeddings of competitors as the constraint. As a result, HM$^{2}$2 can explore companies’ intrinsic properties to improve the CRV performance. Extensive experiments on real-world data demonstrate the effectiveness of the proposed HM$^{2}$2.

Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data, which emerges unknown classes sequentially. Different from traditional closed set learning, CIL has two main challenges: (1) Novel class detection. The initial training data only contains incomplete classes, and streaming test data will accept unknown classes. Therefore, the model needs to not only accurately classify known classes, but also effectively detect unknown classes; (2) Model expansion. After the novel classes are detected, the model needs to be updated without re-training using the entire previous data. However, traditional CIL methods have not fully considered these two challenges. First, they are always restricted to single novel class detection within each phase and embedding confusion caused by unknown classes. Besides, they ignore the catastrophic forgetting of known categories in model update. To this end, we propose a semi-supervised style Class-Incremental Learning without Forgetting (CILF) method, which aims to learn adaptive embedding for processing novel class detection and model update in a unified framework. In detail, CILF designs to regularize classification with decoupled prototype based loss, which can improve the intra-class and inter-class structure significantly, and acquires a compact embedding representation for novel class detection in result. Then, CILF employs a learnable curriculum clustering operator to estimate the number of semantic clusters via fine-tuning the learned network, in which curriculum operator can adaptively learn the embedding in self-taught form. Therefore, CILF can detect multiple novel classes and mitigate the embedding confusion problem. Last, with the labeled streaming test data, CILF can update the network with robust regularization to mitigate the catastrophic forgetting. Consequently, CILF is able to iteratively perform novel class detection and model update. We verify the effectiveness of our model on four streaming classification tasks, and empirical studies show the superior performance of the proposed method.

Person-Organization fit (P-O fit) refers to the compatibility between employees and their organizations. The study of P-O fit is important for enhancing proactive talent management. While considerable efforts have been made in this direction, it still lacks a quantitative and holistic way for measuring P-O fit and its impact on talent management. To this end, in this paper, we propose a novel data-driven neural network approach for dynamically modeling the compatibility in P-O fit and its meaningful relationships with two critical issues in talent management, namely talent turnover and job performance. Specifically, inspired by the practical management scenarios, we creatively propose a novel neural-network-based P-O fit model. We first designed three kinds of organization-aware compatibility features extraction layers for measuring P-O fit. Then, to capture the dynamic nature of P-O fit and its consequent impact, we further exploit an adapted Recurrent Neural Network with attention mechanism to model the temporal information of P-O fit. Finally, we compare our approach with a number of state-of-the-art baseline methods on real-world talent data. Experimental results clearly demonstrate the effectiveness in terms of turnover and job performance prediction. Moreover, we show some interesting indicators of talent management through the visualizing some network layers.

Artificial Intelligence (AI) is an integral part of Society 5.0. Increasingly large companies are using AI technology to promote digital transformation and assist the human resource management (HRM) process to achieve sustainable talent development. Talent intelligent management system (TIMS), as a typical hybrid intelligent system, brings both opportunities and challenges to the transformation of HRM. Although the use of TIMS has met the expectation of Society 5.0 on replacing repetitive work by digital technology, practical insights are needed to address the potential challenges in human replacement, tacit knowledge of managers and data-driven recommendations, and low interpretability of TIMS recommendations. This article outlines possible solutions to cope with the above challenges. This article shows that the challenges brought by TIMS can be solved by different mechanisms, which cover improving users’ trust in TIMS, balancing the rational results and irrational emotions, and enhancing the interpretability of AI algorithms.

Causal inference has recently garnered significant interest among recommender system (RS) researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple fields. It offers a framework to model the causality in recommender systems like confounding effects and deal with counterfactual problems such as offline policy evaluation and data augmentation. Although there are already some valuable surveys on causal recommendations, they typically classify approaches based on the practical issues faced in RS, a classification that may disperse and fragment the unified causal theories. Considering RS researchers' unfamiliarity with causality, it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective, thereby facilitating a deeper integration of causal inference in RS. This survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal strategy. Firstly, we introduce the fundamental concepts of causal inference as the basis of the following review. Subsequently, we propose a novel theory-driven taxonomy, categorizing existing methods based on the causal theory employed—namely, those based on the potential outcome framework, the structural causal model, and general counterfactuals. The review then delves into the technical details of how existing methods apply causal inference to address particular recommender issues. Finally, we highlight some promising directions for future research in this field. Representative papers and open-source resources will be progressively available at https://github.com/Chrissie-Law/Causal-Inference-for-Recommendation.

Business districts are urban areas that have various functions for gathering people, such as work, consumption, leisure and entertainment. Due to the dynamic nature of business activities, there exists significant tidal effect on the boundary and functionality of business districts. Indeed, effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a city. However, with the implicit and complex nature of business district evolution, it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business districts. To this end, we propose a data-driven and multi-dimensional framework for dynamic business district analysis. Specifically, we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time periods. Then, we detect and forecast the functional changes in business districts. Experimental results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business districts. Moreover, the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business districts. For example, the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.

Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logsfor short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.

The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on underlying infrastructure. However, in a subway environment, such positioning systems are not available for the positioning tasks, such as the detection of the train arrivals for the passengers in the train. An alternative approach is to exploit the contextual information available in the mobile devices of subway riders to detect train arrivals. To this end, we propose to exploit multiple contextual features extracted from the mobile devices of subway riders to precisely detecting train arrivals. Following this line, we first investigate potential contextual features which may be effective to detect train arrivals according to the observations from 3D accelerometers and GSM radio. Furthermore, we propose to explore the maximum entropy (MaxEnt) model for training a train arrival detector by learning the correlation between contextual features and train arrivals. Finally, we perform extensive experiments on several real-world data sets collected from two major subway lines in the Beijing subway system. Experimental results validate both the effectiveness and efficiency of the proposed approach.

With the rapid development of online Knowledge Sharing Communities (KSCs), the problem of finding experts becomes increasingly important for knowledge propagation and putting crowd wisdom to work. A recent development trend of KSCs is to allow users to add text tags for annotating their posts, which are more accurate than traditional category information. However, how to leverage these user-generated tags for finding experts is still underdeveloped. To this end, this paper develops a novel approach for finding experts in tag based KSCs by leveraging tag context and the semantic relationship between tags. Specifically, the extracted prior knowledge and user profiles are first used for enriching the query tags to infer tag context, which represents the user’s latent information needs. Specifically, two different approaches for addressing the problem of tag sparseness in authority ranking are proposed. The first is a memory-based collaborative filtering approach, which leverages non-negative matrix factorization (NMF) to find similar users for alleviating tag sparseness. The second approach is based on Latent Dirichlet Allocation (LDA) topic model, which can further capture the latent semantic relationship between tags. A large-scale real-world data set is collected from a tag based Chinese commercial Q&A web site. Experimental results show that the proposed method outperforms several baseline methods with a significant margin.