Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation

In this paper, we study the problem of Team Member Replacement: given a team of people embedded in a social network working on the same task, find a good candidate who can fit in the team after one team member becomes unavailable. We conjecture that a good team member replacement should have good skill matching as well as good structure matching. We formulate this problem using the concept of graph kernel. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall; and (b) scale sub-linearly.Comment: Initially submitted to KDD 201

Construction of Trust Relationship between Doctors and Patients: A Social Psychological Analysis

Doctor-patient trust is the basis of harmonious doctor-patient relationship. Social psychology plays a unique role in interpreting the connotation and construction of doctor-patient trust relationship. From the two levels of doctor-patient interpersonal trust and intergroup trust, this paper summarizes the relevant theoretical viewpoints of social psychology on the construction of doctor-patient trust relationship, and analyzes the key factors affecting doctor-patient interpersonal trust and intergroup trust. On this basis, this paper puts forward the construction path of doctor-patient trust of “interpersonal interaction-emotional communication-interpersonal trust” and “intergroup interaction-social knowledge-intergroup trust”, reveals the interaction mechanism of interpersonal trust and intergroup trust and the circular feedback mechanism between them to promote the formation of doctor-patient trust relationship, and establishes a social psychology model of the formation mechanism of doctor-patient trust relationship

Modeling User Viewing Flow using Large Language Models for Article Recommendation

This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles. Specifically, we employ a user constant viewing flow modeling method to summarize the user's general interest to recommend articles. We utilize Large Language Models (LLMs) to capture constant user preferences from previously clicked articles, such as skills and positions. Then we design the user instant viewing flow modeling method to build interactions between user-clicked article history and candidate articles. It attentively reads the representations of user-clicked articles and aims to learn the user's different interest views to match the candidate article. Our experimental results on the Alibaba Technology Association (ATA) website show the advantage of SINGLE, which achieves 2.4% improvements over previous baseline models in the online A/B test. Our further analyses illustrate that SINGLE has the ability to build a more tailored recommendation system by mimicking different article viewing behaviors of users and recommending more appropriate and diverse articles to match user interests.Comment: 8 pages

Finding Needles in Heterogeneous Haystacks

Due to intense competition and lack of real estate on the front page of large e-commerce platforms, sellers are sometimes motivated to garner non-genuine signals (clicks, add-to-carts, purchases) on their products, to make them appear more appealing to customers. This hurts customers' trust on the platform, and also hurts genuine sellers who sell their items without looking to game the system. While it is important to find the sellers and the buyers who are colluding to garner these non-genuine signals, doing so is highly nontrivial. Firstly, the set of bad actors in the system is a very small fraction of all the buyers/sellers on the platform. Secondly, bad actors ``hide" with the good ones, making them hard to detect. In this paper, we develop CONGCN, a context aware heterogeneous graph convolutional network to detect bad actors on a large heterogeneous graph. While our method is motivated by abuse detection in e-commerce, the method is applicable to other areas such as computational biology and finance, where large heterogeneous graphs are pervasive, and the amount of labeled data is very limited. We train CONGCN via novel sampling methods, and context aware message passing in a semi-supervised fashion to predict dishonest buyers and sellers in e-commerce. Extensive experiments show that our method is effective, beating several baselines; generalizable to an inductive setting and highly scalabl

Does social media users’ commenting behavior differ by their local community tie? A computer–assisted linguistic analysis approach

This study is an exploratory attempt to use automatic linguistic analysis for understanding social media users’ news commenting behavior. The study addresses geographically–based dynamics in human–computer interaction, namely, users’ tie to a geographic community. Specifically, the study reveals that commenting behavior differs between users of different levels of local community tie. Comments by local users, those with higher level of local community tie, exhibit different linguistic patterns in comparison to national users who are less involved in local community. The linguistic differences are reflected in the use of pronouns, personal pronouns, social words, swear words, anxiety words and anger words. We argue that identification of the difference is crucial in the practice of mining social media conversations for public opinion

Learning Optimal Propagation for Graph Neural Networks

Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph might not be optimal in terms of specific downstream tasks, because of information scarcity, noise, adversarial attacks, or discrepancies between the distribution in graph topology, features, and groundtruth labels. In this paper, we propose a bi-level optimization-based approach for learning the optimal graph structure via directly learning the Personalized PageRank propagation matrix as well as the downstream semi-supervised node classification simultaneously. We also explore a low-rank approximation model for further reducing the time complexity. Empirical evaluations show the superior efficacy and robustness of the proposed model over all baseline methods.Comment: 7 pages, 3 figure

SMINet: State-Aware Multi-Aspect Interests Representation Network for Cold-Start Users Recommendation

Online travel platforms (OTPs), e.g., bookings.com and Ctrip.com, deliver travel experiences to online users by providing travel-related products. Although much progress has been made, the state-of-the-arts for cold-start problems are largely sub-optimal for user representation, since they do not take into account the unique characteristics exhibited from user travel behaviors. In this work, we propose a State-aware Multi-aspect Interests representation Network (SMINet) for cold-start users recommendation at OTPs, which consists of a multi-aspect interests extractor, a co-attention layer, and a state-aware gating layer. The key component of the model is the multi-aspect interests extractor, which is able to extract representations for the user's multi-aspect interests. Furthermore, to learn the interactions between the user behaviors in the current session and the above multi-aspect interests, we carefully design a co-attention layer which allows the cross attentions between the two modules. Additionally, we propose a travel state-aware gating layer to attentively select the multi-aspect interests. The final user representation is obtained by fusing the three components. Comprehensive experiments conducted both offline and online demonstrate the superior performance of the proposed model at user representation, especially for cold-start users, compared with state-of-the-art methods