The basic machine for grinding a steel ball bearing has been the same since around 1900, but manufacturers have been steadily automating everything around it. Today, the process is driven by a conveyor belt, and, for the most part, it's automatic. The most urgent task for humans is to figure out when things are going wrong--and even that could soon be handed over to AI. The Schaeffler factory in Hamburg starts with steel wire that is cut and pressed into rough balls. Those balls are hardened in a series of furnaces, and then put through three increasingly precise grinders until they are spherical to within a tenth of a micron.

Users and creators are two crucial components of recommender systems. Typical recommender systems focus on the user side, providing the most suitable items based on each user's request. In such scenarios, a few items receive a majority of exposures, while many items receive very few. This imbalance leads to poorer experiences and decreased activity among the creators receiving less feedback, harming the recommender system in the long term. To this end, we develop a creator-side recommender system, called DualRec, to answer the following question: how to find the most suitable users for each item to enhance the creators' experience? We show that typical user-side recommendation algorithms, such as retrieval and ranking algorithms, can be adapted into the creator-side versions with just a few modifications. This greatly simplifies algorithm design in DualRec. Moreover, we discuss a unique challenge in DualRec: the user availability issue, which is not present in user-side recommender systems. To tackle this issue, we incorporate a user availability calculation (UAC) module to effectively enhance DualRec's performance. DualRec has already been implemented in Kwai, a short video recommendation system with over 100 millions user and over 10 million creators, significantly improving the experience for creators.

Recommender systems play a pivotal role in modern content platforms, yet traditional behavior-based models often face challenges in addressing cold users with sparse interaction data. Engaging these users, however, remains critical for sustaining platform growth. To tackle this issue, we propose leveraging reconstructed social graph to complement interest representations derived from behavioral data. Despite the widespread availability of social graphs on content platforms, their utility is hindered by social-relation noise and inconsistencies between social and behavioral interests. To mitigate noise propagation in graph data and extract reliable social interests, we introduce a dual-view denoising framework. This approach first applies low-rank singular value decomposition (SVD) to the user-item interaction matrix, generating denoised user embeddings for reconstructing the social graph. It then employs contrastive learning to align the original and reconstructed social graphs. To address the discrepancy between social and behavioral interests, we utilize a mutual distillation mechanism that decomposes interests into four subcategories: aligned social/behavioral interests and social/behavioral-specific interests, enabling effective integration of the two. Empirical results demonstrate the efficacy of our method, particularly in improving recommendations for cold users, by combining social and behavioral data. The implementation of our approach is publicly available at https://github.com/WANGLin0126/CLSRec.

The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' becomes regulated globally, it is increasingly important to develop mechanisms that delete user data from AI systems while maintaining performance and scalability of these systems. Incremental Unlearning (IU) is a promising MU solution to address the challenges of efficiently removing specific data from ML models without the need for expensive and time-consuming full retraining. This paper presents the various techniques and approaches to IU. It explores the challenges faced in designing and implementing IU mechanisms. Datasets and metrics for evaluating the performance of unlearning techniques are discussed as well. Finally, potential solutions to the IU challenges alongside future research directions are offered. This survey provides valuable insights for researchers and practitioners seeking to understand the current landscape of IU and its potential for enhancing privacy-preserving intelligent systems.

Grindr, the dating app that caters to gay men, cannot be held responsible for the rape of a 15-year-old boy who the company matched with sexual predators, the U.S. 9th Circuit Court of Appeals ruled this week; it is the latest teens-versus-tech spat in a fight over internet immunity experts say could soon come before the U.S. Supreme Court. The appellate court's ruling upheld a 2023 decision by U.S. District Judge Otis D. Wright II of the Central District of California, who dismissed the suit, saying Grindr was shielded by broad immunity protections passed almost a decade before the plaintiff was born. In a series of events Wright called "alarming and tragic," a closeted Nova Scotia teen downloaded the LGBTQ hookup app in an attempt to meet other gay kids in his rural Canadian town. Instead, over the course of four days, he was assaulted by four adult men, including a man who picked him up after the teen sent him pictures from his high school cafeteria. LGBTQ social networking platform Grindr last year told its all-remote staff they had to return to the office or lose their jobs.

Conversational agents are increasingly woven into individuals' personal lives, yet users often underestimate the privacy risks involved. The moment users share information with these agents (e.g., LLMs), their private information becomes vulnerable to exposure. In this paper, we characterize the notion of contextual privacy for user interactions with LLMs. It aims to minimize privacy risks by ensuring that users (sender) disclose only information that is both relevant and necessary for achieving their intended goals when interacting with LLMs (untrusted receivers). Through a formative design user study, we observe how even "privacy-conscious" users inadvertently reveal sensitive information through indirect disclosures. Based on insights from this study, we propose a locally-deployable framework that operates between users and LLMs, and identifies and reformulates out-of-context information in user prompts. Our evaluation using examples from ShareGPT shows that lightweight models can effectively implement this framework, achieving strong gains in contextual privacy while preserving the user's intended interaction goals through different approaches to classify information relevant to the intended goals.

Cross-domain recommendation (CDR) is a task that aims to improve the recommendation performance in a target domain by leveraging the information from source domains. Contrastive learning methods have been widely adopted among intra-domain (intra-CL) and inter-domain (inter-CL) users/items for their representation learning and knowledge transfer during the matching stage of CDR. However, we observe that directly employing contrastive learning on mixed-up intra-CL and inter-CL tasks ignores the difficulty of learning from inter-domain over learning from intra-domain, and thus could cause severe training instability. Therefore, this instability deteriorates the representation learning process and hurts the quality of generated embeddings. To this end, we propose a novel framework named SCCDR built up on a separated intra-CL and inter-CL paradigm and a stop-gradient operation to handle the drawback. Specifically, SCCDR comprises two specialized curriculum stages: intra-inter separation and inter-domain curriculum scheduling. The former stage explicitly uses two distinct contrastive views for the intra-CL task in the source and target domains, respectively. Meanwhile, the latter stage deliberately tackles the inter-CL tasks with a curriculum scheduling strategy that derives effective curricula by accounting for the difficulty of negative samples anchored by overlapping users. Empirical experiments on various open-source datasets and an offline proprietary industrial dataset extracted from a real-world recommender system, and an online A/B test verify that SCCDR achieves state-of-the-art performance over multiple baselines.

Universities serve as a hub for academic collaboration, promoting the exchange of diverse ideas and perspectives among students and faculty through interdisciplinary dialogue. However, as universities expand in size, conventional networking approaches via student chapters, class groups, and faculty committees become cumbersome. To address this challenge, an academia-specific profile recommendation system is proposed to connect like-minded stakeholders within any university community. This study evaluates three techniques: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid approach to generate effective recommendations. Due to the unlabelled nature of the dataset, Affinity Propagation cluster-based relabelling is performed to understand the grouping of similar profiles. The hybrid model demonstrated superior performance, evidenced by its similarity score, Silhouette score, Davies-Bouldin index, and Normalized Discounted Cumulative Gain (NDCG), achieving an optimal balance between diversity and relevance in recommendations. Furthermore, the optimal model has been implemented as a mobile application, which dynamically suggests relevant profiles based on users' skills and collaboration interests, incorporating contextual understanding. The potential impact of this application is significant, as it promises to enhance networking opportunities within large academic institutions through the deployment of intelligent recommendation systems.

Large language models have become a powerful method for feature augmentation in recommendation systems. However, existing approaches relying on quick inference often suffer from incomplete feature coverage and insufficient specificity in feature descriptions, limiting their ability to capture fine-grained user preferences and undermining overall performance. Motivated by the recent success of inference scaling in math and coding tasks, we explore whether scaling inference can address these limitations and enhance feature quality. Our experiments show that scaling inference leads to significant improvements in recommendation performance, with a 12% increase in NDCG@10. The gains can be attributed to two key factors: feature quantity and specificity. In particular, models using extended Chain-of-Thought (CoT) reasoning generate a greater number of detailed and precise features, offering deeper insights into user preferences and overcoming the limitations of quick inference. We further investigate the factors influencing feature quantity, revealing that model choice and search strategy play critical roles in generating a richer and more diverse feature set. This is the first work to apply inference scaling to feature augmentation in recommendation systems, bridging advances in reasoning tasks to enhance personalized recommendation.

In recent years recommendation systems typically employ the edge information provided by knowledge graphs combined with the advantages of high-order connectivity of graph networks in the recommendation field. However, this method is limited by the sparsity of labels, cannot learn the graph structure well, and a large number of noisy entities in the knowledge graph will affect the accuracy of the recommendation results. In order to alleviate the above problems, we propose a dynamic knowledge-selecting and evaluating method guided by collaborative signals to distill information in the knowledge graph. Specifically, we use a Chain Route Evaluator to evaluate the contributions of different neighborhoods for the recommendation task and employ a Knowledge Selector strategy to filter the less informative knowledge before evaluating. We conduct baseline model comparison and experimental ablation evaluations on three public datasets. The experiments demonstrate that our proposed model outperforms current state-of-the-art baseline models, and each modules effectiveness in our model is demonstrated through ablation experiments.