RecAD: Towards A Unified Library for Recommender Attack and Defense

In recent years, recommender systems have become a ubiquitous part of our daily lives, while they suffer from a high risk of being attacked due to the growing commercial and social values. Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments. To address this, we propose RecAD, a unified library aiming at establishing an open benchmark for recommender attack and defense. RecAD takes an initial step to set up a unified benchmarking pipeline for reproducible research by integrating diverse datasets, standard source codes, hyper-parameter settings, running logs, attack knowledge, attack budget, and evaluation results. The benchmark is designed to be comprehensive and sustainable, covering both attack, defense, and evaluation tasks, enabling more researchers to easily follow and contribute to this promising field. RecAD will drive more solid and reproducible research on recommender systems attack and defense, reduce the redundant efforts of researchers, and ultimately increase the credibility and practical value of recommender attack and defense. The project is released at https://github.com/gusye1234/recad

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. E.g., if the system keeps exposing and recommending the items that the user is interested in, it may also make the user feel bored and less satisfied. Existing work studies filter bubbles in static recommendation, where the effect of overexposure is hard to capture. In contrast, we believe it is more meaningful to study the issue in interactive recommendation and optimize long-term user satisfaction. Nevertheless, it is unrealistic to train the model online due to the high cost. As such, we have to leverage offline training data and disentangle the causal effect on user satisfaction. To achieve this goal, we propose a counterfactual interactive recommender system (CIRS) that augments offline reinforcement learning (offline RL) with causal inference. The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction. It then uses the learned causal user model to help the planning of the RL policy. To conduct evaluation offline, we innovatively create an authentic RL environment (KuaiEnv) based on a real-world fully observed user rating dataset. The experiments show the effectiveness of CIRS in bursting filter bubbles and achieving long-term success in interactive recommendation. The implementation of CIRS is available via https://github.com/chongminggao/CIRS-codes.Comment: 11 pages, 9 figure

Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation

Conversational recommendation systems (CRS) effectively address information asymmetry by dynamically eliciting user preferences through multi-turn interactions. Existing CRS widely assumes that users have clear preferences. Under this assumption, the agent will completely trust the user feedback and treat the accepted or rejected signals as strong indicators to filter items and reduce the candidate space, which may lead to the problem of over-filtering. However, in reality, users' preferences are often vague and volatile, with uncertainty about their desires and changing decisions during interactions. To address this issue, we introduce a novel scenario called Vague Preference Multi-round Conversational Recommendation (VPMCR), which considers users' vague and volatile preferences in CRS.VPMCR employs a soft estimation mechanism to assign a non-zero confidence score for all candidate items to be displayed, naturally avoiding the over-filtering problem. In the VPMCR setting, we introduce an solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two main components: Uncertainty-aware Soft Estimation (USE) and Uncertainty-aware Policy Learning (UPL). USE estimates the uncertainty of users' vague feedback and captures their dynamic preferences using a choice-based preferences extraction module and a time-aware decaying strategy. UPL leverages the preference distribution estimated by USE to guide the conversation and adapt to changes in users' preferences to make recommendations or ask for attributes. Our extensive experiments demonstrate the effectiveness of our method in the VPMCR scenario, highlighting its potential for practical applications and improving the overall performance and applicability of CRS in real-world settings, particularly for users with vague or dynamic preferences

Generating reliable friends via adversarial training to improve social recommendation

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems. However, this assumption is often untenable as the online social networks are quite sparse and a majority of users only have a small number of friends. Besides, explicit friends may not share similar interests because of the randomness in the process of building social networks. Therefore, discovering a number of reliable friends for each user plays an important role in advancing social recommendation. Unlike other studies which focus on extracting valuable explicit social links, our work pays attention to identifying reliable friends in both the observed and unobserved social networks. Concretely, in this paper, we propose an end-to-end social recommendation framework based on Generative Adversarial Nets (GAN). The framework is composed of two blocks: a generator that is used to produce friends that can possibly enhance the social recommendation model, and a discriminator that is responsible for assessing these generated friends and ranking the items according to both the current user and her friends' preferences. With the competition between the generator and the discriminator, our framework can dynamically and adaptively generate reliable friends who can perfectly predict the current user' preference at a specific time. As a result, the sparsity and unreliability problems of explicit social relations can be mitigated and the social recommendation performance is significantly improved. Experimental studies on real-world datasets demonstrate the superiority of our framework and verify the positive effects of the generated reliable friends

Failure Evolution Law of Reinforced Anchor System under Pullout Load Based on DIC

To obtain the failure evolution law, a pullout test model of the anchor system is proposed based on the digital image correlation (DIC) measurements. By the study of the displacement field, the strain field, and the force transfer law of the anchor system under the pulling load, the failure law of the anchor system is revealed. The results show that (1) the failure mode and the ultimate bearing capacity of the anchor system are related to the thickness of the anchor agent; (2) in the anchor system, the pulling force is gradually transferred from the loading end to the free end along the steel bar, and the greater the thickness of the anchoring agent, the deeper the transfer range; (3) during the loading, the deformation of the anchoring system is mainly concentrated at the interface between the anchoring agent and the concrete and expands to the depth along the steel bar; and (4) the failure evolution rate of the anchorage system is related to the loading stage. The failure evolution of the anchor system can be divided into the elastic phase, the plastic phase, and the deformation rebound phase

BLOMA: explain collaborative filtering via Boosted Local rank-One Matrix Approximation

Matrix Approximation (MA) is a powerful technique in recommendation systems. There are two main problems in the prevalent MA framework. First, the latent factor is out of explanation and hampers the understanding of the reasons behind recommendations. Besides, traditional MA methods produce user/item factors globally, which fails to capture the idiosyncrasies of users/items. In this paper, we propose a model called Boosted Local rank-One Matrix Approximation (BLOMA). The core idea is to locally and sequentially approximate the residual matrix (which represents the unexplained part obtained from the previous stage) by rank-one sub-matrix factorization. The result factors are distinct and explainable by leveraging social networks and item attributes

Interactive active learning for fairness with partial group label

The rapid development of AI technologies has found numerous applications across various domains in human society. Ensuring fairness and preventing discrimination are critical considerations in the development of AI models. However, incomplete information often hinders the complete collection of sensitive attributes in real-world applications, primarily due to the high cost and potential privacy violations associated with such data collection. Label reconstruction through building another learner on sensitive attributes is a common approach to address this issue. However, existing methods focus solely on improving the prediction accuracy of the sensitive learner as a separate model, while ignoring the disparity between its accuracy and the fairness of the base model. To bridge this gap, this paper proposes an interactive learning framework that aims to optimize the sensitive learner while considering the fairness of the base learner. Furthermore, a new active sampling strategy is developed to select the most valuable data for the sensitive learner regarding the fairness of the base model. The effectiveness of our proposed method in improving model fairness is demonstrated through comprehensive evaluations conducted on various datasets and fairness criteria

Semantic trajectory representation and retrieval via hierarchical embedding

Trajectory mining has gained growing attention due to its emerging applications, such as location-based services, urban computing, and movement behavior analyses. One critical and fundamental mining task is to retrieve specific locations or trajectories that satisfy particular patterns. However, existing approaches mainly represent the trajectory as a collection of geographic and temporal features, so the latent semantic properties are barely considered. In this paper, we introduce a new semantic trajectory representation method, which considers trajectory structures, temporal information, and domain knowledge to make efficient semantic retrieval possible. Specifically, we first introduce a synchronization-based model to identify multi-resolution regions of interest (ROIs) to extract structures from disordered raw trajectories. Afterward, we proposed a hierarchical embedding model to embed ROIs as well as trajectories on the hierarchical ROI network as continuous vectors by considering multiple kinds of semantic similarity. As a result, users can easily retrieve desirable ROIs or trajectories by computing the similarity among embedded vectors. Experiments show that our approach excels both classical trajectory metric-based models and state-of-the-art deep network embedding models in terms of retrieving interpretable ROIs and trajectories

Associations between Body Mass Index and Visual Impairment of School Students in Central China

Body Mass Index (BMI) is a risk indicator for some eye diseases. However, the association between BMI and Visual Impairment (VI) was not quite certain in Chinese students. Our aim was to assess the relationship between BMI and VI with a cross-sectional study. A total of 3771 students aged 6–21 years, including 729 with VI, were sampled from 24 schools in Huangpi District of central China to participate in the study. A multistage stratified cluster random sampling was adopted. Each of the students answered a questionnaire and had physical and eye examinations. The association between BMI and VI was examined with logistic regression and threshold effect analysis. The prevalence of VI was 19.33% (729/3771). Compared to normal and underweight, overweight/obese students showed a stronger relation with VI in age- and sex-adjusted (Odds Ratio (OR) = 16.16, 95% Confidence Interval (CI): 12.37–21.09, p < 0.001) and multivariable models (OR = 8.32, 95% CI: 6.13–11.30, p < 0.001). There was a nonlinear dose–response relation between levels of BMI and the prevalence of VI (p < 0.001). A high level of BMI (≥19.81 kg/m2) was associated with a higher VI prevalence (adjusted OR = 1.20, 95% CI: 1.15–1.25, p < 0.001). In conclusion, the study demonstrated BMI levels were significantly associated with the prevalence of VI

Social media usage of chinese nursing students: Attitudes, motivations, mental health problems, and self-disclosure.

BackgroundExcessive self-disclosure online may risk the reputations, mental health problems, and professional lives of nursing students. This study investigated nursing students' usage of social media, their attitudes towards social media, mental health problems and self-disclosures, and the relationships of these variables.MethodsA cross-sectional study was conducted online (n = 1054) with questionnaires of Fear of Missing Out (FoMO), Social Media Fatigue (SMF), Students' Uses and Views of Social Media (SUVSM) and self-disclosure in social media which included self-information shown on social media and information viewed by others.ResultsAlthough most of them held positive attitudes towards social media, 17.4% of the participants acknowledged that they had posted inappropriate contents online and 37.6% witnessed improper posts from schoolmates or teachers online. SMF was affected by familiar with relevant regulations on the social media usage (β = -.10, p ConclusionInappropriate contents are posted and witnessed by appreciable proportions of nursing students. Positive attitude towards social media may strengthen FoMO and SMF, which may increase self-disclosure in social media in turn