Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference

Selection bias in recommender system arises from the recommendation process of system filtering and the interactive process of user selection. Many previous studies have focused on addressing selection bias to achieve unbiased learning of the prediction model, but ignore the fact that potential outcomes for a given user-item pair may vary with the treatments assigned to other user-item pairs, named neighborhood effect. To fill the gap, this paper formally formulates the neighborhood effect as an interference problem from the perspective of causal inference and introduces a treatment representation to capture the neighborhood effect. On this basis, we propose a novel ideal loss that can be used to deal with selection bias in the presence of neighborhood effect. We further develop two new estimators for estimating the proposed ideal loss. We theoretically establish the connection between the proposed and previous debiasing methods ignoring the neighborhood effect, showing that the proposed methods can achieve unbiased learning when both selection bias and neighborhood effect are present, while the existing methods are biased. Extensive semi-synthetic and real-world experiments are conducted to demonstrate the effectiveness of the proposed methods.

A Survey of Generative Search and Recommendation in the Era of Large Language Models

With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs. As the two sides of the same coin, both revolve around the same core research problem, matching queries with documents or users with items. In the recent few decades, search and recommendation have experienced synchronous technological paradigm shifts, including machine learning-based and deep learning-based paradigms. Recently, the superintelligent generative large language models have sparked a new paradigm in search and recommendation, i.e., generative search (retrieval) and recommendation, which aims to address the matching problem in a generative manner. In this paper, we provide a comprehensive survey of the emerging paradigm in information systems and summarize the developments in generative search and recommendation from a unified perspective. Rather than simply categorizing existing works, we abstract a unified framework for the generative paradigm and break down the existing works into different stages within this framework to highlight the strengths and weaknesses. And then, we distinguish generative search and recommendation with their unique challenges, identify open problems and future directions, and envision the next information-seeking paradigm.

Exact and Efficient Unlearning for Large Language Model-based Recommendation

The evolving paradigm of Large Language Model-based Recommendation (LLMRec) customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) using recommendation data. The inclusion of user data in LLMs raises privacy concerns. To protect users, the unlearning process in LLMRec, specifically removing unusable data (e.g., historical behaviors) from established LLMRec models, becomes crucial. However, existing unlearning methods are insufficient for the unique characteristics of LLM-Rec, mainly due to high computational costs or incomplete data erasure. In this study, we introduce the Adapter Partition and Aggregation (APA) framework for exact and efficient unlearning while maintaining recommendation performance. APA achieves this by establishing distinct adapters for partitioned training data shards and retraining only the adapters impacted by unusable data for unlearning. To preserve recommendation performance and mitigate considerable inference costs, APA employs parameter-level adapter aggregation with sample-adaptive attention for individual testing samples. Extensive experiments substantiate the effectiveness and efficiency of our proposed framework

Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model

Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications. However, the effective integration of T2I models into fundamental image classification tasks remains an open question. A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models. In this study, we scrutinize the shortcomings of both current generative and conventional data augmentation techniques. Our analysis reveals that these methods struggle to produce images that are both faithful (in terms of foreground objects) and diverse (in terms of background contexts) for domain-specific concepts. To tackle this challenge, we introduce an innovative inter-class data augmentation method known as Diff-Mix (https://github.com/Zhicaiwww/Diff-Mix), which enriches the dataset by performing image translations between classes. Our empirical results demonstrate that Diff-Mix achieves a better balance between faithfulness and diversity, leading to a marked improvement in performance across diverse image classification scenarios, including few-shot, conventional, and long-tail classifications for domain-specific datasets.

Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients

Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information. However, existing approaches often suffer from fairness issues, as recommendations tend to be more accurate for patients with common diseases compared to those with rare conditions. In this paper, we propose a novel model called Robust and Accurate REcommendations for Medication (RAREMed), which leverages the pretrain-finetune learning paradigm to enhance accuracy for rare diseases. RAREMed employs a transformer encoder with a unified input sequence approach to capture complex relationships among disease and procedure codes. Additionally, it introduces two self-supervised pre-training tasks, namely Sequence Matching Prediction (SMP) and Self Reconstruction (SR), to learn specialized medication needs and interrelations among clinical codes. Experimental results on two real-world datasets demonstrate that RAREMed provides accurate drug sets for both rare and common disease patients, thereby mitigating unfairness in medication recommendation systems.

Boosting Few-Shot Learning via Attentive Feature Regularization

Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from different categories with a blending factor. However, this mixing operation weakens the feature representation due to the linear interpolation and the overlooking of the importance of specific channels. To solve these issues, this paper proposes attentive feature regularization (AFR) which aims to improve the feature representativeness and discriminability. In our approach, we first calculate the relations between different categories of semantic labels to pick out the related features used for regularization. Then, we design two attention-based calculations at both the instance and channel levels. These calculations enable the regularization procedure to focus on two crucial aspects: the feature complementarity through adaptive interpolation in related categories and the emphasis on specific feature channels. Finally, we combine these regularization strategies to significantly improve the classifier performance. Empirical studies on several popular FSL benchmarks demonstrate the effectiveness of AFR, which improves the recognition accuracy of novel categories without the need to retrain any feature extractor, especially in the 1-shot setting. Furthermore, the proposed AFR can seamlessly integrate into other FSL methods to improve classification performance.

Proactive Recommendation with Iterative Preference Guidance

Recommender systems mainly tailor personalized recommendations according to user interests learned from user feedback. However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization. To counteract this, proactive recommendation actively steers users towards developing new interests in a target item or topic by strategically modulating recommendation sequences. Existing work for proactive recommendation faces significant hurdles: 1) overlooking the user feedback in the guidance process; 2) lacking explicit modeling of the guiding objective; and 3) insufficient flexibility for integration into existing industrial recommender systems. To address these issues, we introduce an Iterative Preference Guidance (IPG) framework. IPG performs proactive recommendation in a flexible post-processing manner by ranking items according to their IPG scores that consider both interaction probability and guiding value. These scores are explicitly estimated with iteratively updated user representation that considers the most recent user interactions. Extensive experiments validate that IPG can effectively guide user interests toward target interests with a reasonable trade-off in recommender accuracy. The code is available at https://github.com/GabyUSTC/IPG-Rec.

The 2nd Workshop on Recommendation with Generative Models

The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender algorithms, (ii) generating personalized content, (iii) evolving the user-system interaction paradigm, (iv) enhancing trustworthiness checks, and (v) refining evaluation methodologies for generative recommendations. With generative models advancing rapidly, an increasing body of research is emerging in these domains, underscoring the timeliness and critical importance of this workshop. The related research will introduce innovative technologies to recommender systems and contribute to fresh challenges in both academia and industry. In the long term, this research direction has the potential to revolutionize the traditional recommender paradigms and foster the development of next-generation recommender systems.

Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning

Traditional recommendation setting tends to excessively cater to users' immediate interests and neglect their long-term engagement. To address it, it is crucial to incorporate planning capabilities into the recommendation decision-making process to develop policies that take into account both immediate interests and long-term engagement. Despite Reinforcement Learning (RL) can learn planning capacity by maximizing cumulative reward, the scarcity of recommendation data presents challenges such as instability and susceptibility to overfitting when training RL models from scratch. In this context, we propose to leverage the remarkable planning capabilities over sparse data of Large Language Models (LLMs) for long-term recommendation. The key lies in enabling a language model to understand and apply task-solving principles effectively in personalized recommendation scenarios, as the model's pre-training may not naturally encompass these principles, necessitating the need to inspire or teach the model. To achieve this, we propose a Bi-level Learnable LLM Planner framework, which combines macro-learning and micro-learning through a hierarchical mechanism. The framework includes a Planner and Reflector for acquiring high-level guiding principles and an Actor-Critic component for planning personalization. Extensive experiments validate the superiority of the framework in learning to plan for long-term recommendations.

DiFashion: Towards Personalized Outfit Generation and Recommendation

The evolution of Outfit Recommendation (OR) in the realm of fashion has progressed through two distinct phases: Pre-defined Outfit Recommendation and Personalized Outfit Composition. Despite these advancements, both phases face limitations imposed by existing fashion products, hindering their effectiveness in meeting users' diverse fashion needs. The emergence of AI-generated content has paved the way for OR to overcome these constraints, demonstrating the potential for personalized outfit generation. In pursuit of this, we introduce an innovative task named Generative Outfit Recommendation (GOR), with the goal of synthesizing a set of fashion images and assembling them to form visually harmonious outfits customized to individual users. The primary objectives of GOR revolve around achieving high fidelity, compatibility, and personalization of the generated outfits. To accomplish these, we propose DiFashion, a generative outfit recommender model that harnesses exceptional diffusion models for the simultaneous generation of multiple fashion images. To ensure the fulfillment of these objectives, three types of conditions are designed to guide the parallel generation process and Classifier-Free-Guidance are employed to enhance the alignment between generated images and conditions. DiFashion is applied to both personalized Fill-In-The-Blank and GOR tasks, and extensive experiments are conducted on the iFashion and Polyvore-U datasets. The results of quantitative and human-involved qualitative evaluations highlight the superiority of DiFashion over competitive baselines.