Predicting the scientific impact of research scholars is increasingly crucial for career planning, particularly for young scholars considering career transitions. However, predicting a scholar's future development, especially after they move to a different academic group, presents significant challenges. To tackle this issue, we propose a Future Publication Impact Prediction Network (FPIPN) based on graph neural networks. FPIPN leverages rich information from a heterogeneous academic graph for impact prediction. We employ a hierarchical attention mechanism to learn the significance of graph information and utilize a knowledge distillation strategy to assess future impact based on historical records. Extensive experiments on a real-world academic dataset showcase the effectiveness of our approach compared to state-of-the-art methods.

Knowledge discovery and utilization are two essential cognitive processes that enable humans to understand the world and extract new insights from their surroundings. These processes have motivated machine learning studies, particularly zero-shot (ZS) learning, which seeks to identify unseen concepts through the use of side information. Previous ZS studies primarily focused on utilizing existing knowledge to infer unseen events, yet they overlook the crucial process of knowledge discovery and the integrated modeling of these knowledge-aware processes. In this study, we present a comprehensive ZS learning approach that explores and evaluates the machine’s abilities of discovering and utilizing knowledge. More specifically, to emulate human-like knowledge discovery and utilization processes, we propose a novel visual-aware ZS knowledge graph completion task for evaluation, incorporating a traditional ZS image classification task. Technically, we develop a unified ZS learning paradigm named Cognitive Learner (CoLa) to foster the two knowledge-aware abilities. Including a knowledge representation learning (KRL) module and a knowledge adaptation (KA) module, CoLa adapts well to the two specified tasks with the corresponding data. Extensive experiments on large-scale datasets demonstrate CoLa models’ outstanding performance over compared methods in the two ZS tasks, illustrating their superior ability of discovering and utilizing knowledge.

Recent years have witnessed the increasing interests in research of crowdfunding mechanism. In this area, dynamics tracking is a significant issue but is still under exploration. Existing studies either fit the fluctuations of time-series or employ regularization terms to constrain learned tendencies. However, few of them take into account the inherent decision-making process between investors and crowdfunding dynamics. To address the problem, in this paper, we propose a Trajectory-based Continuous Control for Crowdfunding (TC3) algorithm to predict the funding progress in crowdfunding. Specifically, actor-critic frameworks are employed to model the relationship between investors and campaigns, where all of the investors are viewed as an agent that could interact with the environment derived from the real dynamics of campaigns. Then, to further explore the in-depth implications of patterns (i.e., typical characters) in funding series, we propose to subdivide them into fast-growing and slow-growing ones. Moreover, for the purpose of switching from different kinds of patterns, the actor component of TC3 is extended with a structure of options, which comes to the TC3-Options. Finally, extensive experiments on the Indiegogo dataset not only demonstrate the effectiveness of our methods, but also validate our assumption that the entire pattern learned by TC3-Options is indeed the U-shaped one.

Cross-domain sentiment classification refers to utilizing useful knowledge in the source domain to help sentiment classification in the target domain which has few or no labeled data. Most existing methods mainly concentrate on extracting common features between domains. Unfortunately, they cannot fully consider the effects of the aspect (e.g., the battery life in reviewing an electronic product) information of the sentences. In order to better solve this problem, we propose an Interactive Attention Transfer Network (IATN) for crossdomain sentiment classification. IATN provides an interactive attention transfer mechanism, which can better transfer sentiment across domains by incorporating information of both sentences and aspects. Specifically, IATN comprises two attention networks, one of them is to identify the common features between domains through domain classification, and the other aims to extract information from the aspects by using the common features as a bridge. Then, we conduct interactive attention learning for those two networks so that both the sentences and the aspects can influence the final sentiment representation. Extensive experiments on the Amazon reviews dataset and crowdfunding reviews dataset not only demonstrate the effectiveness and universality of our method, but also give an interpretable way to track the attention information for sentiment.

This paper introduces Alpha-Beta Sampling (ABS) strategy, which is particularly intended for the sampling problem of pairwise ranking in one-class collaborative filtering (PROCCF). Specifically, ABS strategy places more emphasis on such training examples, including positive item with a lower preference score and negative items with a higher preference score for each gradient step. Then, we provide the corresponding proofs for the ABS strategy from both gradient and ranking perspectives. First, we prove that sampled training examples by ABS strategy can update the model parameters with a large magnitude and analyze two instantiations by combining two specific pairwise algorithms. Second, it can be proved that ABS strategy is equivalent to optimizing for ranking-aware evaluation metrics like Normalized Discounted Cumulative Gain (NDCG). Furthermore, ABS strategy can be very general and applicable in a lot of pairwise structures of pairwise algorithms. Based on ABS strategy, we provide an effective sampling algorithm to dynamically draw items for each SGD update. Finally, we evaluate the ABS strategy by conducting sampling tasks in two representative pairwise algorithms. The experiment results show that the ABS strategy performs significantly better than the baseline strategies.

Crowdfunding is an emerging Internet fundraising mechanism by raising monetary contributions from the crowd for projects or ventures. In these platforms, the dynamics, i.e., daily funding amount on campaigns and perks (backing options with rewards), are the most concerned issue for creators, backers and platforms. However, tracking the dynamics in crowdfunding is very challenging and still under-explored. To that end, in this paper, we present a focused study on this important problem. A special goal is to forecast the funding amount for a given campaign and its perks in the future days. Specifically, we formalize the dynamics in crowdfunding as a hierarchical time series, i.e., campaign level and perk level. Specific to each level, we develop a special regression by modeling the decision making process of the crowd (visitors and backing probability) and exploring various factors that impact the decision; on this basis, an enhanced switching regression is proposed at each level to address the heterogeneity of funding sequences. Further, we employ a revision matrix to combine the two-level base forecasts for the final forecasting. We conduct extensive experiments on a real-world crowdfunding data collected from Indiegogo.com. The experimental results clearly demonstrate the effectiveness of our approaches on tracking the dynamics in crowdfunding.

P2P lending is an emerging Internet-based application where individuals can directly borrow money from each other. The past decade has witnessed the rapid development and prevalence of online P2P lending platforms, examples of which include Prosper, LendingClub, and Kiva. Meanwhile, extensive research has been done that mainly focuses on the studies of platform mechanisms and transaction data. In this article, we provide a comprehensive survey on the research about P2P lending, which, to the best of our knowledge, is the first focused effort in this field. Specifically, we first provide a systematic taxonomy for P2P lending by summarizing different types of mainstream platforms and comparing their working mechanisms in detail. Then, we review and organize the recent advances on P2P lending from various perspectives (e.g., economics and sociology perspective, and data-driven perspective). Finally, we propose our opinions on the prospects of P2P lending and suggest some future research directions in this field. Meanwhile, throughout this paper, some analysis on real-world data collected from Prosper and Kiva are also conducted.