Object removal aims to eliminate undesired objects from images while plausibly restoring the underlying content with high visual fidelity. Existing diffusion-based methods often achieve strong results by relying on large-scale model finetuning, auxiliary control networks, or powerful diffusion backbones, which incur substantial computational overhead and limit practical efficiency. In this work, we propose SlimNet, a high-quality and efficient object removal framework that elicits the latent object removal capabilities of pretrained diffusion models. SlimNet injects lightweight adapters into a frozen diffusion backbone to selectively modulate intermediate representations, avoiding heavy architectural modifications. To further improve visual fidelity, we design a composite perceptual loss that enforces object removal accuracy, background preservation, and smooth boundary transitions, together with a semantic-aware data processing pipeline for automatic mask generation. Extensive experiments demonstrate that SlimNet achieves competitive removal quality compared to state-of-the-art methods, with favorable reductions in inference time and VRAM consumption, making it a practical solution for resource-constrained multimedia applications.

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 .

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.

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.

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.

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.

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.