Multi-behavior recommendation systems enhance prediction accuracy for target behavior (e.g., purchase) by integrating auxiliary behaviors such as viewing and adding to cart. However, existing models face two fundamental challenges regarding data characteristics: On one hand, they typically treat all auxiliary behaviors equally or apply simple weighting, lacking effective mechanisms to evaluate and filter inherent noise, leading to contaminated feature representations. On the other hand, when fusing sparse target features with abundant auxiliary features, the critical target signal is easily diluted, losing its dominant role in the final prediction. To address this, we propose a Target-Enhanced Gated Transformer model TEGT. Its core innovations include: a behavior-adaptive gating module that filters source-level noise through nonlinear transformations and hard thresholding while generating importance weights; a target-guided dual-modulation attention mechanism utilizes target behavior as queries to retrieve semantically relevant auxiliary signals, then applies secondary modulation by combining gated weights to ensure both noise resistance and target dominance in the fusion process; a lightweight collaborative semantic enhancement module clusters the fused representations and employs cluster-center contrastive learning to explicitly amplify collaborative signals under sparse target behavior. Extensive experiments on three real-world datasets show that TEGT consistently outperforms state-of-the-art baselines, it achieves remarkable improvements of up to 9.73% in Recall@10 and 9.16% in NDCG@10.

Major promotional events such as Black Friday and 618 Shopping Day cause drastic, heterogeneous shifts in user and advertiser behavior, posing persistent challenges for conversion rate (CVR) models trained on daily data. Existing methods often lack the flexibility to capture this periodic variability, resulting in poor modeling of diverse behavioral patterns. To address these challenges, we propose TemporalExpertNet(TEN), a cross-temporal transfer learning framework for industrial-scale CVR prediction during promotion cycles. TEN decomposes the model into a stable representation encoder and a promotion-sensitive expert, enabling reusable temporal knowledge transfer. Specifically, we propose BridgeNet to address the mismatch between historical knowledge and current features through temporal representation alignment. We further introduce TemporalExpertGate (TEG) to perform sample-aware expert fusion, enabling fine-grained prediction adjustment and adaptive knowledge reuse across promotion periods. By using a two-stage training strategy, TEN achieves stable alignment and adaptive expert fusion for robust prediction under shifting promotional distributions. TEN was deployed on a large-scale short-video ads platform during the 618 preheating phase, improving conversion rate by 7.52% and platform RPM by 4.27% with only 0.23% model size and 1.8% latency overhead. It was therefore fully launched to all traffic on 618 Shopping Day, bringing substantial commercial gains.

Existing LLM-driven recommendation systems (RS) suffer from over-reliance on a single pre-trained model, which limits adaptability across diverse scenarios due to differences in large language models' strengths in semantics, knowledge, and reasoning. To address this issue, we propose AMLrec (Adaptive Multi-LLM Recommendation), a dynamic routing-based adaptive multi-LLM collaboration framework that unifies two dominant paradigms—LLM as Recommender and LLM + Recommender—through decision knowledge complementation. For each user or item, a lightweight encoder generates embeddings that are compared with learnable LLM prototypes using cosine similarity to select the most suitable models. In the first paradigm, selected LLMs generate recommendations via structured prompts, and their outputs are aggregated to form the final recommendation list. In the second paradigm, chosen LLMs produce semantic embeddings, which are fused with learnable embeddings after PCA-based dimensionality reduction and aligned using a lightweight adapter to bridge distribution gaps. Notably, AMLrec does not require fine-tuning of the underlying LLMs, significantly reducing computational overhead. Experiments on real-world datasets demonstrate that the proposed approach consistently outperforms single-LLM baselines across all evaluation metrics, validating its effectiveness. The main contributions of this work are threefold: introducing dynamic routing for multi-LLM recommendation system collaboration, proposing a unified architecture that harmonizes both paradigms, and enabling efficient adaptation without LLM fine-tuning. The code is available at https://github.com/Jiale-12138/AMLrec.

The proliferation of online publications and interdisciplinary studies has presented researchers with the challenge of sifting through a substantial volume of articles to identify citations that substantiate their research ideas. Consequently, the development of citation recommendation technology has become a pivotal aspect of product promotion and marketing for academic support platforms. Traditionally, citation recommendation has primarily relied either on collaborative signals derived from paper interactions or on content-based similarity—both of which are essential for identifying relevant references. However, existing basic strategies often focus on one of these aspects while neglecting the other, leading to suboptimal performance in capturing the complex factors behind citation behavior. The reason is that the integration of domain characteristics in scholarly fields and the mining of semantic relevance in text information is also crucial for modelling researchers’ preferences. In this work, we present a novel citation recommendation model called SCTRec, that aligns S equential C ollaborative signals from publications’ indexes, that is, IDs, and T ext semantic content for citation Rec ommendation. To address the actual technical challenges encountered, such as shifts in user preferences and semantic gaps between IDs and texts, we have designed a hybrid enhancement mechanism that bridges these semantic gaps, thereby learning more discriminative feature representations. The effectiveness of SCTRec in enhancing citation recommendation performance is substantiated by extensive experimental evaluation on multiple public datasets. The code is available on Anonymous Github at https://github.com/guaiqihen/SCTRec .

Multi-behavior recommender systems improve prediction accuracy of target behaviors (e.g., purchases) by integrating auxiliary behaviors (e.g., page views). However, existing models face two key limitations: (1) Static propagation mechanisms and inflexible dependency modeling fail to capture dynamic changes in user preferences and cascading relationships between behaviors; (2) Sparse target behavior data usually leads to excessive influence of auxiliary signals, which degrades recommendation quality. To address these challenges, we propose the Dynamic Dependency-Aware Collaborative Contrastive Learning Multi-Behavior Recommendation Model, MBDCC. MBDCC has two dedicated modules: (1) Behavioral gating cascade and cross-attention fusion module, which dynamically models cascading dependencies between behaviors through learnable gate control transfer units controlled by behavioral attributes. This replaces static propagation with adaptive feature flow regulation, capturing evolving user preferences. Meanwhile, it uses a target-guided cross-attention mechanism to selectively fuse semantically relevant auxiliary signals using the target behavior as a query, addressing inflexible cross-behavioral dependency modeling; (2) Collaborative semantic enhancement module, it constructs a user similarity measure matrix based on co-occurrence frequency of interaction items in target behavior, and clusters nodes using a hybrid clustering strategy. By introducing contrastive learning between nodes and their clustering centers, the collaborative semantic information between similar nodes under the target behavior is effectively captured and amplified, alleviating the challenge of sparse target interaction data. Extensive experiments on three real-world datasets show that MBDCC consistently outperforms state-of-the-art baselines, it achieves remarkable improvements of up to 6.84% in Recall@10 and 5.18% in NDCG@50. Moreover, ablation experiments further demonstrate the correctness of our motivation and the necessity of the various modules of the MBDCC model.

Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language, with an increasing presence in the field of recommendation systems. However, LLMs still encounter a significant issue known as prompt sensitivity, where the model’s output is susceptible to minor changes in the input prompt. This challenge is particularly problematic in recommendation systems, which rely on stable and consistent outputs. Fine-tuning LLMs with diverse prompts can reduce prompt sensitivity but also lead to a decline in recommendation performance. Therefore, choosing an effective fine-tuning method is important to achieve enhanced robustness without sacrificing performance. To address prompt sensitivity while maintaining recommendation performance, we propose Generative Adversarial Network-based prompt enhancement (GANPrompt), a framework for improving LLM-based recommendation systems using adversarial game theory. In this framework, the generator and discriminator compete to produce diverse prompts, which are then used to fine-tune LLM-based recommendation systems, enhancing both robustness and accuracy. Specifically, to generate diverse prompts for fine-tuning and enhance the robustness of LLMs, we develop a GAN-based generator for diverse prompts, with an attribute generation module providing the foundational data support. Further, we introduce a diversity constraint to ensure that the generated prompts maintain high diversity while preserving semantic consistency. To maintain accuracy during the fine-tuning process, we introduce an explicit guidance knowledge token integration method. This method reduces noise and information loss in the face of diverse prompts by enhancing the use of traditional collaborative signals. Through extensive experiments on four publicly available datasets and one real-world industrial dataset, we demonstrate the effectiveness of the proposed framework. Our source code is available at https://github.com/LxytIUON/GANPrompt .

In the realm of recommender systems, accurately predicting Click-Through Rate (CTR) is a critical task that involves learning user-item interaction features. Many researchers propose novel models to mine interaction signals, but they neglect that redundancy itself causes high computational cost and leads to suboptimal performance. Some tried to remove redundancy by dropping useless features, or shrinking the size of embedding table. However, current feature selection methods are vulnerable to training stochasticity and data dynamics, while embedding size assignment techniques neglect the importance relationships between feature fields. The simple combination of the two optimization ways will also yield poor performance due to the inherent gap in their optimization targets. Hence, there is no effective paradigm that can optimize feature fields from the two aspects in a simultaneous and coordinated way. In this paper, we identify the core issue as the lack of a practical score to measure the contribution of feature fields, and propose a distribution-based field optimization framework that adopts importance distribution to provide a comprehensive view for both methods. We innovatively design a learner for each field to acquire the stable and comprehensive importance situation. Then, based on this, we eliminate noise features, and assign adaptive embedding sizes for different feature fields according to the similarity of importance. With this field optimization, our proposed framework has extremely low pre-training overhead, greatly reduces training and inference time, and even achieves more accurate prediction results with fewer feature fields.

An intelligent code search engine tries to find and suggest a code piece given a developer's query quickly from a large-scale program database, which can significantly promote software development efficiency. Existing solutions can search the relevant codes to some extent. However, most of them fail to precisely understand the search intent of developers since they only mine their natural language queries, while ignoring the valuable programming context (e.g., the code written by the developer). In this paper, we study the novel problem of context-aware code search. To promote a step forward, we first provide the CodeSearchNet-C dataset with constructing sufficient programming context from the GitHub website for each query-code instance. The dataset is supplemented on the CodeSearchNet benchmark, ensuring both generality and comparability for relevant research. Then, by analyzing the characteristics of programming context, we propose a novel two-stage Context-aware Code Retrieval (ConCR) framework. In the first stage, we propose a Context Walking algorithm, which simulates the programming habits of different developers. The generated programming context could ensure the diversity of search intent among developers. In the second stage, imitating the reading habits of developers, we introduce a novel Context Hierarchical Encoder, to understand the search intent with contextual information from local to global. Our ConCR framework is general, and we give three implementations on the basis of typical code search models as backbones. Extensive experimental results clearly prove that our ConCR significantly enhances the code search performance, effectively fulfilling developers' needs for efficient code resource searching on the web. These results also verify the necessity of introducing programming context to understand developers' intent.

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.

Large Language Models (LLMs) are increasingly prominent in the recommendation systems domain. Existing studies usually utilize in-context learning or supervised finetuning on task-specific data to align LLMs into recommendations. However, the substantial bias in semantic spaces between language processing tasks and recommendation tasks poses a non-negligible challenge. Specifically, without the adequate capturing ability of collaborative information, existing modeling paradigms struggle to capture behavior patterns within community groups, leading to LLMs' ineffectiveness in discerning implicit interaction semantic in recommendation scenarios. To address this, we consider enhancing the learning capability of language modeldriven recommendation models for structured data, specifically by utilizing interaction graphs rich in collaborative semantics. We propose a Graph-Aware Learning for Language ModelDriven Recommendations (GAL-Rec). GAL-Rec enhances the understanding of user-item collaborative semantics by imitating the intent of Graph Neural Networks (GNNs) to aggregate multihop information, thereby fully exploiting the substantial learning capacity of LLMs to independently address the complex graphs in the recommendation system. Sufficient experimental results on three real-world datasets demonstrate that GAL-Rec significantly enhances the comprehension of collaborative semantics, and improves recommendation performance.

Despite advancements using graph neural networks (GNNs) to capture complex user-item interactions, challenges persist due to data sparsity and noise. To address these, self-supervised learning (SSL) methods, particularly recent generative approaches, have gained attention due to their ability to augment graph data without requiring complex view constructions and unstable negative sampling. However, existing generative SSL solutions often focus on structural rather than semantic (refer to collaborative signals in recommendation scenarios) reconstruction, limiting their potential as comprehensive recommender. This paper explores the untapped potential of generative SSL for graph-based recommender systems. We highlight two critical challenges: firstly, designing effective diffusion mechanisms to enhance semantic information and collaborative signals while avoiding optimization biases; and secondly, developing adaptive structural masking mechanisms within graph diffusion to improve overall model performance. Motivated by these challenges, we propose a novel approach: the Guided Diffusion enhanced Mask graph AutoEncoder (GDiffMAE). GDiffMAE integrates an adaptive mask encoder for structural reconstruction and a guided diffusion model for semantic reconstruction, addressing the limitations of current methods. Experimental results on diverse datasets demonstrate that GDiffMAE consistently outperforms powerful baseline models, particularly in handling noisy data scenarios. By enhancing both structural and semantic dimensions through guided diffusion, our model advances the state-of-the-art in graph-based recommender systems.

Recently, the multi-behavior information on a specific domain has been successfully exploited by aggregating diverse user behaviors to solve the problems of cold start and data sparsity in recommendations. However, the user behavior information captured from multiple behaviors in a single domain is insufficient. Our study seeks to enhance user behavior prediction by leveraging both multi-behavior information and cross-domain information in a more effective manner. In order to explore the correlations and differences between different behaviors and different domains, we propose a novel competition framework consists of intra-domain competition and inter-domain competition for knowledge learning. Specifically, for intra-domain, a behavior competition mechanism is designed to enable the model to mine users’ interests and behavior patterns effectively. For inter-domain, a domain competition mechanism is designed to perform knowledge transfer and knowledge fusion for overlapping users in different domains. Through the competition mechanisms, our proposedGraph Competitive Transfer Network (GCTN)achieves knowledge transfer between different domains and captures users’ behavior patterns in different contexts. The effectiveness of the GCTN and its competition mechanisms has been validated through sufficient experimental trials onDoubanandAmazondatasets. Compared to baseline methods, GCTN has demonstrated a marked improvement in both$AUC$and$F1$scores.

Zero-Shot Relational Learning (ZSRL), strives to predict relations that have not been observed during training, presenting a considerable challenge in terms of model generalization. Existing ZSRL methods usually utilize the prior knowledge of labels (e.g., text description, ontological schema) to enable knowledge transfer by learned features. Nonetheless, these methods remain limited to calculating the surface features exhibited by relations, failing to fully explore their underlying driving factors. This leads to insufficient discrimination between the shared and distinctive inherent components among relations, which consequently impedes the cognitive understanding required for advanced reasoning. In our study, we aim to identify and utilize shared factors that widely exist in the prior knowledge of classes to learn enhanced semantic representations via shared factors composition, and develop our Factor-based ZSRL framework (FZR) with Generative Adversarial Networks (GANs) to bridge inequality between seen and unseen classes. FZR is designed to restructure the semantic space in such a way that it captures the essence of relation formation, thereby facilitating superior knowledge transfer in zero-shot scenarios. We conduct extensive experiments and evaluate our model on real-world datasets, and the results clearly demonstrate the effectiveness of the proposed model in zero-shot relational learning tasks.

To alleviate the long-standing data sparsity issue in recommender systems, numerous studies in cross-domain recommendation (CDR) have been conducted to facilitate information transfer processes across domains. In recent years, dual-target CDR has been introduced to gain mutual improvements between two domains through more general bidirectional transfer rather than traditional one-way transit. Existing methods in dual-target CDR focus primarily on designing powerful encoders to learn representative cross-domain information, without tackling the fundamental issue of interaction data shortage. In this paper, we present CrossAug, a novel data augmentation approach to leverage interactions more efficiently in two domains. Specifically, we propose intra-domain and inter-domain augmentations based on cross-reconstructed representations in terms of sampled records. To reduce the harm of domain shift, we project domain-shared representations in two domains into a joint space with Householder transformations and apply center alignments. All these modules boost the utilization of interactions with little influence from negative transfer. Extensive experimental results over public datasets demonstrate the effectiveness of CrossAug and its components in dual-target CDR.

In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to cart, and purchasing. Multi-behavior sequential recommendation aims to jointly consider multiple behaviors to improve the target behavior's performance. However, with diversified behavior data, user behavior sequences will become very long in the short term, which brings challenges to the efficiency of the sequence recommendation model. Meanwhile, some behavior data will also bring inevitable noise to the modeling of user interests. To address the aforementioned issues, firstly, we develop the Efficient Behavior Sequence Miner (EBM) that efficiently captures intricate patterns in user behavior while maintaining low time complexity and parameter count. Secondly, we design hard and soft denoising modules for different noise types and fully explore the relationship between behaviors and noise. Finally, we introduce a contrastive loss function along with a guided training strategy to contrast the valid information with the noisy signal in the data, and seamlessly integrate the two denoising processes to achieve a high degree of decoupling of the noisy signal. Sufficient experiments on real-world datasets demonstrate the effectiveness and efficiency of our approach in dealing with multi-behavior sequential recommendation.

With the explosive growth of scholarly information, researchers spend much time and effort copiously quoting authoritative works to support their ideas or motivations. We aim to alleviate this situation by proposing a citation recommendation strategy that recalls related papers for a rough idea (a piece of text, i.e., abstract, manuscript). However, the perspective of existing citation recommendations can not be well applied to our task for two defects. First, these methods neglect the reasoning of research topics, which makes the recommendation mechanism not meticulous enough and lacks explainability. For instance, they are not able to mine the hidden citing logic for the candidate paper while recommending. We fill the research gap by constructing structural topics consisting of knowledge concepts from the textual content, where reasoning paths between topics are extracted from an external knowledge graph. Second, the citation network is viewed as a crucial structural context to enhance the recommendation performance, but the new target idea does not have links to the citation network as published papers do. To simulate the prospective topological structure, our model, meanwhile, incorporates a contrastive-learning-based alignment paradigm to encourage the consistency of content embeddings and structure-oriented embeddings. We evaluate our proposed model on three real-world datasets and demonstrate that it significantly improves recommendation accuracy while providing high-quality knowledge-aware reasoning. And an interesting visual example illustrates the reasoning process when our model actually judges samples, which supports the feasibility of our topic-view learning paradigm.

In recent years, multi-behavior information has been utilized to address data sparsity and cold-start issues. The general multi-behavior models capture multiple behaviors of users to make the representation of relevant features more fine-grained and informative. However, most current multi-behavior recommendation methods neglect the exploration of social relations between users. Actually, users’ potential social connections are critical to assist them in filtering multifarious messages, which may be one key for models to tap deeper into users’ interests. Additionally, existing models usually focus on the positive behaviors (e.g., click , follow , and purchase ) of users and tend to ignore the value of negative behaviors (e.g., unfollow and badpost ). In this work, we present a Multi-Behavior Graph (MBG) construction method based on user behaviors and social relationships and then introduce a novel socially enhanced and behavior-aware graph neural network for behavior prediction. Specifically, we propose a Socially Enhanced Heterogeneous Graph Convolutional Network (SHGCN) model, which utilizes behavior heterogeneous graph convolution module and social graph convolution module to effectively incorporate behavior features and social information to achieve precise multi-behavior prediction. In addition, the aggregation pooling mechanism is suggested to integrate the outputs of different graph convolution layers, and a dynamic adaptive loss (DAL) method is presented to explore the weight of each behavior. The experimental results on the datasets of the e-commerce platforms (i.e., Epinions and Ciao) indicate the promising performance of SHGCN. Compared with the most powerful baseline, SHGCN achieves 3.3% and 1.4% uplift in terms of AUC on the Epinions and Ciao datasets. Further experiments, including model efficiency analysis, DAL mechanism, and ablation experiments, confirm the validity of the multi-behavior information and social enhancement.

Recommendation systems have attracted attention from academia and industry due to their wide range of application scenarios. However, cold start remains a challenging problem limited by sparse user interactions. Some scholars propose to transfer the dense information from the source domain to the target domain through cross-domain recommendation, but most of the work assumes that there is a small amount of historical interaction in the target domain. However, this approach essentially presupposes the existence of at least some historical interaction within the target domain. In this paper, we focus on the domain-level zero-shot recommendation (DZSR) problem. To address the above challenges, we propose a knowledge-aware cross-semantic alignment (K-CSA) framework to learn transferable source domain semantic information. The motivation is to establish stable alignments of interests in different domains through class semantic descriptions (CSDs). Specifically, due to the lack of effective information in the target domain, we learn semantic representations of source and target domain items based on knowledge graphs. Moreover, we conduct multi-view K-means to extract item CSDs from the learned semantic representations. Further, K-CSA learns universal user CSDs through the designed multi-head self-attention. To facilitate the transference of user interest from the source domain to the target domain, we devise a cross-semantic contrastive learning strategy, grounded in the prototype distribution matrix. We conduct extensive experiments on several real-world cross-domain datasets, and the experimental results clearly demonstrate the superiority of our proposed K-CSA compared with other baselines.

The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the considerable achievements, existing methods usually focus on intra-sequence modeling while overlooking exploiting global collaborative information by inter-sequence modeling, resulting in inferior recommendation performance. Therefore, previous works attempt to tackle this problem with a global collaborative item graph constructed by pre-defined rules. However, these methods neglect two crucial properties when capturing global collaborative information, i.e., adaptiveness and personalization, yielding sub-optimal user representations. To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems. Specifically, we first learn an adaptive global graph among all items and capture global collaborative information with it in a self-supervised fashion, whose computational burden can be further alleviated by the proposed SVD-based accelerator. Furthermore, based on the graph, we propose to extract and utilize personalized item correlations in the form of relative positional encoding, which is a highly compatible manner of personalizing the utilization of global collaborative information. Finally, the entire framework is optimized in a multi-task learning paradigm, thus each part of APGL4SR can be mutually reinforced. As a generic framework, APGL4SR can not only outperform other baselines with significant margins, but also exhibit promising versatility, the ability to learn a meaningful global collaborative graph, and the ability to alleviate the dimensional collapse issue of item embeddings.

Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously. Recently, the Knowledge Graph (KG) has been proven as an effective scheme for handling the zero-shot task with large-scale and non-attribute data. Prior studies always embed relationships of seen and unseen objects into visual information from existing knowledge graphs to promote the cognitive ability of the unseen data. Actually, real-world knowledge is naturally formed by multimodal facts. Compared with ordinary structural knowledge from a graph perspective, multimodal KG can provide cognitive systems with fine-grained knowledge. For example, the text description and visual content can depict more critical details of a fact than only depending on knowledge triplets. Unfortunately, this multimodal fine-grained knowledge is largely unexploited due to the bottleneck of feature alignment between different modalities. To that end, we propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings via a designed dense attention module and self-calibration loss. It makes the semantic transfer process of our ZSL framework learns more differentiated knowledge between entities. Our model also gets rid of the performance limitation of only using rough global features. We conduct extensive experiments and evaluate our model on large-scale real-world data. The experimental results clearly demonstrate the effectiveness of the proposed model in standard zero-shot classification tasks.

Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCNs) can effectively learn node representations via aggregating neighbors based on the relation graph. However, despite a few exceptions, most of the previous work in this line does not consider the topical semantics underlying the edges, making the node representations less effective and the learned relation between nodes hard to explain. For instance, the current GNNs make us usually don’t know what is the reason for the connection of network nodes, such as the specific research topics cited in this article and the concerns among friends on social platforms. Some methods have begun to explore the extraction of relation semantics in recent related literature, but existing studies generally face two bottlenecks, i.e., either being unable to explain the mined latent relations to ensure their reasonableness and independence, or demanding the textual content of edges which is unavailable in most real-world datasets. Actually, these two issues are both crucial in practical use. In our work, we propose a novel Topic-Disentangled Graph Neural Network (TDG) to address the above two issues at the same time, which explores the relation topics from the perspective of node contents. We design an optimized graph topic module to handle node features to construct independent and explainable semantic subspaces, then the reasonable relation topics that correspond to these subspaces are assigned to each graph relation via a neighborhood routing mechanism. Our proposed model can be easily combined with related graph tasks to form an end-to-end model, to avoid the risk of deviation between node representation space and task space. To evaluate the efficiency of our model, sufficient node-related tasks are conducted on three public datasets in the experimental section. The results show the obvious superiority of TDG compared with the state-of-the-art models.

Network-aware cascade size prediction aims to predict the final reposted number of user-generated information via modeling the propagation process in social networks. Estimating the user's reposting probability by social influence, namely state activation plays an important role in the information diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate the information interaction between nodes, has been proved as an effective scheme to handle this prediction task. However, existing studies including GNN-based models usually neglect a vital factor of user's preference which influences the state activation deeply. To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate. Extensive experiments on two large-scale real-world datasets have clearly demonstrated the effectiveness of our proposed model compared to state-of-the-art baselines.

Representation learning of examination papers is the cornerstone of the Examination Paper Analysis (EPA) in education area including Paper Difficulty Prediction (PDR) and Finding Similar Papers (FSP). Previous works mainly focus on the representation learning of each test item, but few works notice the hierarchical document structure in examination papers. To this end, in this paper, we propose a novel Examination Organization Encoder (EOE) to learn a robust representation of the examination paper with the hierarchical document structure. Specifically, we first propose a syntax parser to recover the hierarchical document structure and convert an examination paper to an Examination Organization Tree (EOT), where the test items are the leaf nodes and the internal nodes are summarization of their child nodes. Then, we applied a two-layer GRU-based module to obtain the representation of each leaf node. After that, we design a subtree encoder module to aggregate the representation of each leaf node, which is used to calculate an embedding for each layer in the EOT. Finally, we feed all the layer embedding into an output module, the process is over and we get the examination paper representation that can be used for downstream tasks. Extensive experiments on real-world data demonstrate the effectiveness and interpretability of our method.