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 .

Advanced healthcare predictions offer significant improvements in patient outcomes by leveraging predictive analytics. Existing works primarily utilize various views of Electronic Health Record (EHR) data, such as diagnoses, lab tests, or clinical notes, for model training. These methods typically assume the availability of complete EHR views and that the designed model could fully leverage the potential of each view. However, in practice, random missing views and view laziness present two significant challenges that hinder further improvements in multi-view utilization. To address these challenges, we introduce Diffmv, an innovative diffusion-based generative framework designed to advance the exploitation of multiple views of EHR data. Specifically, to address random missing views, we integrate various views of EHR data into a unified diffusion-denoising framework, enriched with diverse contextual conditions to facilitate progressive alignment and view transformation. To mitigate view laziness, we propose a novel reweighting strategy that assesses the relative advantages of each view, promoting a balanced utilization of various data views within the model. Our proposed strategy achieves superior performance across multiple health prediction tasks derived from three popular datasets, including multi-view and multi-modality scenarios.

Accurate healthcare prediction is essential for improving patient outcomes. Existing work primarily leverages advanced frameworks like attention or graph networks to capture the intricate collaborative (CO) signals in electronic health records. However, prediction for rare diseases remains challenging due to limited co-occurrence and inadequately tailored approaches. To address this issue, this paper proposes UDC, a novel method that unveils discrete clues to bridge consistent textual knowledge and CO signals within a unified semantic space, thereby enriching the representation semantics of rare diseases. Specifically, we focus on addressing two key sub-problems: (1) acquiring distinguishable discrete encodings for precise disease representation and (2) achieving semantic alignment between textual knowledge and the CO signals at the code level. For the first sub-problem, we refine the standard vector quantized process to include condition awareness. Additionally, we develop an advanced contrastive approach in the decoding stage, leveraging synthetic and mixed-domain targets as hard negatives to enrich the perceptibility of the reconstructed representation for downstream tasks. For the second sub-problem, we introduce a novel codebook update strategy using co-teacher distillation. This approach facilitates bidirectional supervision between textual knowledge and CO signals, thereby aligning semantically equivalent information in a shared discrete latent space. Extensive experiments on three datasets demonstrate our superiority.

Owing to the impressive general intelligence of large language models (LLMs), there has been a growing trend to integrate them into recommender systems to gain a more profound insight into human interests and intentions. Existing LLMs-based recommender systems primarily leverage item attributes and user interaction histories in textual format, improving the single task like rating prediction or explainable recommendation. Nevertheless, these approaches underestimate the crucial contribution of traditional collaborative signals in discerning users' profound intentions and disregard the interrelatedness among tasks. To address these limitations, we introduce a novel framework known asCKF, specifically developed to boost multi-task recommendations via personalized collaborative knowledge fusion into LLMs. Specifically, to enhance collaborative signal integration, we develop a meta-network that creates personalized mapping bridges for each user. This enables the seamless incorporation of trained collaborative filtering embeddings into structured prompt templates, significantly boosting the LLM's understanding of user interests. To investigate the intrinsic relationship among diverse recommendation tasks, we develop Multi-LoRA, a new parameter-efficient approach for multi-task optimization, adept at distinctly segregating task-shared and task-specific knowledge. This semantic approach forges a connection between LLMs and recommendation scenarios, while simultaneously enriching the supervisory signal through mutual knowledge transfer among various tasks. Extensive experiments and in-depth robustness analyses across four common recommendation tasks on four large public data sets substantiate our effectiveness.

Healthcare predictions, such as readmission prediction, stand as a cornerstone of societal well-being, exerting a profound influence on individual health outcomes and communal vitality. Existing research primarily employs advanced graph neural networks and sequential algorithms for patient modeling, with a focus on discerning the connections and sequential patterns inherent in Electronic Health Records (EHRs). However, the heterogeneity of entity interactions, the locality of EHR data, and the oversight of target relevance hinder further improvements. To address these limitations, we introduce a novel framework B eyond S equential P atterns (BSP), which facilitates precise healthcare predictions by incorporating tri-contextual information. Specifically, we establish a symptom-driven hypergraph network with four semantic hyperedges tailored to the intricacies of the healthcare scenario, such as ontology. This serves as a global context, tracking the heterogeneous entity collaboration within and across patients. Moreover, we construct an extensive knowledge graph leveraging existing medical databases and large language models. By sampling and refining knowledge subgraphs as local context, we bolster the semantic associations of medical entities from closed-set EHR data to the open world. Finally, we introduce the candidate context, an explicit entity-relation loss. It enforces the neighbor consistency between the target and the representation during optimization, thus accounting for correlations among targets. Extensive experiments and rigorous robustness analysis on five tasks derived from four large medical datasets underscore the BSP’s superiority over the leading baselines, with improvements of 11%, 3%, 11%, 3.5%, and 2% across five tasks, demonstrating the efficacy of incorporating diverse contexts.

Cross-domain recommendation offers a potential avenue for alleviating data sparsity and cold-start problems. Embedding and mapping, as a classic cross-domain research genre, aims to identify a common mapping function to perform representation transformation between two domains via exploiting the supervision signals of overlapping users. Nevertheless, coarse-grained preference representations, non-personalized mapping functions, and excessive reliance on overlapping users limit their performance, especially in scenarios where overlapping users are sparse. To address the aforementioned challenges, we propose a novel C ross-domain transfer of V alence P references via a M eta-optimization approach, namely CVPM . CVPM formalizes cross-domain interest transfer as a hybrid architecture of parametric meta-learning and self-supervised learning, which not only enables a more nuanced transfer of user preferences but also enhances signal quality by incorporating insights from non-overlapping users. Specifically, drawing on in-depth knowledge into user preferences and valence preference theory, we believe that there exists a significant difference between users’ positive preferences and negative behaviors, and thus employ differentiated encoders to learn their distributions. In particular, we further utilize the pre-trained model and item popularity to sample pseudo-interaction items to ensure the integrity of both distributions. To guarantee the personalized preference transfer, we treat each user’s mapping as two parts, the common transformation and the personalized bias, where the network generating the personalized bias is produced by a meta-learner. Furthermore, beyond the supervised loss for overlapping users, we design contrastive tasks for non-overlapping users from both group and individual levels to avoid model skew and enhance the semantic richness of representations. We construct six cross-domain tasks and one cross-system task from 10 data sets assessing model performance under both cold-start and warm-start scenarios. Exhaustive data analysis and extensive experimental results demonstrate the effectiveness and advancement of our proposed framework.

Data bias, e.g., popularity impairs the dynamics of two-sided markets within recommender systems. This overshadows the less visible but potentially intriguing long-tail items that could capture user interest. Despite the abundance of research surrounding this issue, it still poses challenges and remains a hot topic in academic circles. Along this line, in this paper, we developed a re-ranking approach in dynamic settings with fair-exposure optimization driven by strategic agents. Designed for the producer side, the execution of agents assumes content creators can modify item features based on strategic incentives to maximize their exposure. This iterative process entails an end-to-end optimization, employing differentiable ranking operators that simultaneously target accuracy and fairness. Joint objectives ensure the performance of recommendations while enhancing the visibility of tail items. We also leveraged the performativity nature of predictions to illustrate how strategic learning influences content creators to shift towards fairness efficiently, thereby incentivizing features of tail items. Through comprehensive experiments on both public and industrial datasets, we have substantiated the effectiveness and dominance of the proposed method especially on unveiling the potential of tail items.

Cross-domain recommendation, as a cutting-edge technology to settle data sparsity and cold start problems, is gaining increasingly popular. Existing research paradigms primarily focus on leveraging the representation of overlapping entities, such as representation aggregation or cross-domain consistency constraints, to facilitate knowledge transfer and enhance the performance of single-domain or dual-domain recommender systems. Even though these approaches bring significant promotions, they still suffer from optimization bottlenecks when faced with sparse overlapping users, which often occurs in reality. Unlocking the full potential of overlapping user information and exploring novel sources of cross-domain knowledge are pivotal in addressing this challenge effectively. On account of this, this paper proposes an innovative cross-domain recommendation framework, namelySEAGULL, to promote dual-target recommendation performance in line with these two perspectives. We bolster the utilization of overlapping user knowledge and extract non-overlapping user interests by refining the message passing mechanism in a unified heterogeneous cross-domain graph and facilitating the transfer of latent structural relationships among users. Specifically, we first construct the interaction of two domains as a unified cross-domain heterogeneous graph and design a novel attention mechanism to incorporate cross-domain collaboration signals between users and items. Second, we perform user structure alignment from global and local levels to extend semantic transfer and information augmentation. Finally, unlike previous work that directly incorporates mixed cross-domain knowledge, we employ a gentle and progressive cross-domain transfer strategy to reduce empirical risk loss. Extensive experiments on five tasks derived from three data sets fully demonstrate the effectiveness ofSEAGULL.

Making accurate and safe clinical decisions for patients has long been a challenging task. With the proliferation of electronic health records and the rapid advancement of technology, drug recommender systems have emerged as invaluable aids for healthcare professionals, offering precise and secure prescriptions. Among prevailing methods, the exploration of motifs, defined as substructures with specific biological functions, has largely been overlooked. Nevertheless, the substantial impact of the motifs on drug efficacy and patient diseases implies that a more extensive incorporation could potentially improve the recommender systems. In light of this, we introduceDEPOT, an innovative drug recommendation framework developed from a motif-aware perspective. In our approach, we employ chemical decomposition to partition drug molecules into semantic motif-trees and design a structure-aware graph transformer to capture motif collaboration. This innovative practice preserves the topology knowledge and facilitates perception of drug functionality. To delve into the dynamic correlation between motifs and disease progression, we conduct a meticulous investigation from two perspectives: repetition and exploration. This comprehensive analysis allows us to gain valuable insights into the drug turnover, with the former focusing on reusability and the latter on discovering new requirements. We further formulate a historical weighting strategy for drug-drug interaction (DDI) objective, enabling adaptive control over the trade-off between accuracy and safety criteria throughout the training process. Extensive experiments conducted on four data sets validate the effectiveness and robustness ofDEPOT.

Cross-domain recommendation, as an intelligent machine to alleviate data sparsity and cold start problems, has attracted extensive attention from scholars. Existing cross-domain recommendation frameworks usually leverage overlapping entities for knowledge transfer, the most popular of which are information aggregation and consistency maintenance. Despite decent improvements, the neglect of dynamic perspectives, the presence of confounding factors, and the disparities in domain properties inevitably constrain model performance. In view of this, this paper proposes a sequential recommendation framework via adaptive cross-domain knowledge decomposition, namely ARISEN, which focuses on employing adaptive causal learning to improve recommendation performance. Specifically, in order to facilitate sequence transfer, we align the user's behaviour sequences in the source domain and target domain according to the timestamps, expecting to use the abundant semantics of the former to augment the information of the latter. Regarding confounding factor removal, we introduce the causal learning technique and promote it as an adaptive representation decomposition framework on the basis of instrumental variables. For the sake of alleviating the impact of domain disparities, this paper endeavors to employ two mutually orthogonal transformation matrices for information fusion. Extensive experiments and detailed analyzes on large industrial and public data sets demonstrate that our framework can achieve substantial improvements over state-of-the-art algorithms.

Cross-domain recommendation aims to leverage knowledge from multiple domains to alleviate the data sparsity and cold-start problems in traditional recommender systems. One popular paradigm is to employ overlapping user representations to establish domain connections, thereby improving recommendation performance in all scenarios. Nevertheless, the general practice of this approach is to train user embeddings in each domain separately and then aggregate them in a plain manner, often ignoring potential cross-domain similarities between users and items. Furthermore, considering that their training objective is recommendation task-oriented without specific regularizations, the optimized embeddings disregard the interest alignment among user’s views, and even violate the user’s original interest distribution. To address these challenges, we propose a novel cross-domain recommendation framework, namely COAST, to improve recommendation performance on dual domains by perceiving the cross-domain similarity between entities and aligning user interests. Specifically, we first construct a unified cross-domain heterogeneous graph and redefine the message passing mechanism of graph convolutional networks to capture high-order similarity of users and items across domains. Targeted at user interest alignment, we develop deep insights from two more fine-grained perspectives of user-user and user-item interest invariance across domains by virtue of affluent unsupervised and semantic signals. We conduct intensive experiments on multiple tasks, constructed from two large recommendation data sets. Extensive results show COAST consistently and significantly outperforms state-of-the-art cross-domain recommendation algorithms as well as classic single-domain recommendation methods.

With an increasing popularity, Multiplayer Online Battle Arena (MOBA) games where two opposing teams compete against each other, have played a major role in E-sports tournaments. Among game analysis, real-time winning prediction is an important but challenging problem, which is mainly due to the complicated coupling of the overall Confrontation1, the excessive noise of the player’s Movement, and unclear optimization goals. Existing research is difficult to solve this problem in a dynamic, comprehensive and systematic way. In this study, we design a unified framework, namely Winning Tracker (WT), for solving this problem. Specifically, offense and defense extractors are developed to extract the Confrontation of both sides. A well-designed trajectory representation algorithm is applied to extracting individual’s Movement information. Moreover, we design a hierarchical attention mechanism to capture team-level strategies and facilitate the interpretability of the framework. To optimize accurately, we adopt a multi-task learning method to design short-term and long-term goals, which are used to represent immediate state and make end-state prediction respectively. Intensive experiments on a real-world data set demonstrate that our proposed method WT outperforms state-of-the-art algorithms. Furthermore, our work has been practically deployed in real MOBA games, and provided case studies reflecting its outstanding commercial value.