Chen Zhu

Online labor market platforms have rapidly developed over the past few years. The job title, as a crucial piece of observable information, research about its impact on job-related behaviors is lack. In this study, we selected one of the largest blue-collar online labor market platforms in China, constructing a large-scale field experiment on job titles with different degrees of information disclosure depth and breadth generated by the proposed model based on Large language model (LLM), involving more than 50,000 job listings and more than 800,000 users in the platform, to investigate whether the information disclosed in the job title affects the likelihood of the job being viewed and successfully matched. Findings claim the compared with the central route (processing depth information), peripheral route information processing (processing breadth information) is the dominant method on online labor market platforms. Additionally, sufficient processing of breadth information significantly impacts subsequent behaviors.

Job recommender systems are crucial for aligning job opportunities with job-seekers in online job-seeking. However, users tend to adjust their job preferences to secure employment opportunities continually, which limits the performance of job recommendations. The inherent frequency of preference drift poses a challenge to promptly and precisely capture user preferences. To address this issue, we propose a novel session-based framework, BISTRO, to timely model user preference through fusion learning of semantic and behavioral information. Specifically, BISTRO is composed of three stages: 1) coarse-grained semantic clustering, 2) fine-grained job preference extraction, and 3) personalized top-k job recommendation. Initially, BISTRO segments the user interaction sequence into sessions and leverages session-based semantic clustering to achieve broad identification of person-job matching. Subsequently, we design a hypergraph wavelet learning method to capture the nuanced job preference drift. To mitigate the effect of noise in interactions caused by frequent preference drift, we innovatively propose an adaptive wavelet filtering technique to remove noisy interaction. Finally, a recurrent neural network is utilized to analyze session-based interaction for inferring personalized preferences. Extensive experiments on three real-world offline recruitment datasets demonstrate the significant performances of our framework. Significantly, BISTRO also excels in online experiments, affirming its effectiveness in live recruitment settings. This dual success underscores the robustness and adaptability of BISTRO. The source code is available at https://github.com/Applied-Machine-Learning-Lab/BISTRO.

The overwhelming surge of online video platforms has raised an urgent need for social interaction recognition techniques. Compared with simple short-term actions, long-term social interactions in semantic-rich videos could reflect more complicated semantics such as character relationships or emotions, which will better support various downstream applications, e.g., story summarization and fine-grained clip retrieval. However, considering the longer duration of social interactions with severe mutual overlap, involving multiple characters, dynamic scenes, and multi-modal cues, among other factors, traditional solutions for short-term action recognition may probably fail in this task. To address these challenges, in this article, we propose a hierarchical graph-based system, named InteractNet, to recognize social interactions in a multi-modal perspective. Specifically, our approach first generates a semantic graph for each sampled frame with integrating multi-modal cues and then learns the node representations as short-term interaction patterns via an adapted GCN module. Along this line, global interaction representations are accumulated through a sub-clip identification module, effectively filtering out irrelevant information and resolving temporal overlaps between interactions. In the end, the association among simultaneous interactions will be captured and modelled by constructing a global-level character-pair graph to predict the final social interactions. Comprehensive experiments on publicly available datasets demonstrate the effectiveness of our approach compared with state-of-the-art baseline methods.

With the booming of streaming media platforms, viewers now get used to watching dramas and movies via online platforms with more intelligent services. Usually, character relationships may dynamically evolve with stories promoting in long videos. Therefore, automatic tools to capture the social relation evolution among characters are urgently required to enrich the viewing experience. However, most existing works mainly focus on shorter isolated video clips. Considering the development of the plot, they may fail to effectively summarize relationships as holistic semantic representations for the whole video. To deal with these challenges, in this paper, we propose a novel Dynamic-Evolutionary Graph Attention Network (DE-GAT) framework to generate the evolving social relation graph among characters and capture the characters’ relation evolutionary trajectory throughout the entire video. DE-GAT first integrates the multimodal cues, including visual and textual information in each video clip via the graph attention network (GAT). Expanding the temporal receptive field from clip-level to scenario-level, the most relevant factors of the evolution of social relationships can be explored. Eventually, all the scenario-level social graphs are merged to obtain the evolving global social graph for the entire movie. Extensive evaluations on the real-world MovieGraphs dataset have validated the positive impact of temporal receptive field expansion and multimodal cues on capturing evolving social relations.

Generative large language models(LLMs) are proficient in solving general problems but often struggle to handle domain-specific tasks. This is because most of domain-specific tasks, such as personalized recommendation, rely on task-related information for optimal performance. Current methods attempt to supplement task-related information to LLMs by designing appropriate prompts or employing supervised fine-tuning techniques. Nevertheless, these methods encounter the certain issue that information such as community behavior pattern in RS domain is challenging to express in natural language, which limits the capability of LLMs to surpass state-of-the-art domain-specific models. On the other hand, domain-specific models for personalized recommendation which mainly rely on user interactions are susceptible to data sparsity due to their limited common knowledge capabilities. To address these issues, we proposes a method to bridge the information gap between the domain-specific models and the general large language models. Specifically, we propose an information sharing module which serves as an information storage mechanism and also acts as a bridge for collaborative training between the LLMs and domain-specific models. By doing so, we can improve the performance of LLM-based recommendation with the help of user behavior pattern information mined by domain-specific models. On the other hand, the recommendation performance of domain-specific models can also be improved with the help of common knowledge learned by LLMs. Experimental results on three real-world datasets have demonstrated the effectiveness of the proposed method.