A Survey on Fairness-aware Recommender Systems

As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarise existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems.Comment: 27 pages, 9 figure

An Exact Algorithm for Minimum Vertex Cover Problem

In this paper, we propose a branch-and-bound algorithm to solve exactly the minimum vertex cover (MVC) problem. Since a tight lower bound for MVC has a significant influence on the efficiency of a branch-and-bound algorithm, we define two novel lower bounds to help prune the search space. One is based on the degree of vertices, and the other is based on MaxSAT reasoning. The experiment confirms that our algorithm is faster than previous exact algorithms and can find better results than heuristic algorithms

Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation

Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users' historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items), which leads to a phenomenon of information cocoon. Therefore, it is necessary to recommend new items to users. As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as '\textbf{G}NN \textbf{S}ession-based \textbf{N}ew \textbf{I}tem \textbf{R}ecommendation (GSNIR)'. To solve this problem, we propose a dual-intent enhanced graph neural network for it. Due to the fact that new items are not tied to historical sessions, the users' intent is difficult to predict. We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item. To solve the challenge that new items cannot be learned by GNNs, inspired by zero-shot learning (ZSL), we infer the new item representation in GNN space by using their attributes. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users. Experiments on two representative real-world datasets show the superiority of our proposed method. The case study from the real-world verifies interpretability benefits brought by the dual-intent module and the new item reasoning module. The code is available at Github: https://github.com/Ee1s/NirGNNComment: 10 Pages, 6 figures, WWW'202

Dimerization Control in the Self-Assembly Behavior of Copillar[5]arenes Bearing ω‑Hydroxyalkoxy Groups

Two novel copillar[5]­arenes bearing ω-hydroxyalkoxy groups are synthesized and their self-assembly properties are studied by ¹H NMR spectroscopy, specific viscosity, and X-ray measurements. The copillar[5]­arene <b>2b</b> bearing a 6-hydroxyhexyloxy group exhibits a reversible self-assembly behavior, leading to the formation of the self-inclusion monomer and hugging dimers. The reversible self-assembly behavior can be controlled by tuning solvent, temperature, guest, and H-bond interaction. However, the copillar[5]­arene <b>2a</b> bearing a short 4-hydroxybutyloxy group does not show such a self-assembly behavior to the formation of the self-inclusion monomer and hugging dimers

Dimerization Control in the Self-Assembly Behavior of Copillar[5]arenes Bearing ω‑Hydroxyalkoxy Groups

Two novel copillar[5]­arenes bearing ω-hydroxyalkoxy groups are synthesized and their self-assembly properties are studied by ¹H NMR spectroscopy, specific viscosity, and X-ray measurements. The copillar[5]­arene <b>2b</b> bearing a 6-hydroxyhexyloxy group exhibits a reversible self-assembly behavior, leading to the formation of the self-inclusion monomer and hugging dimers. The reversible self-assembly behavior can be controlled by tuning solvent, temperature, guest, and H-bond interaction. However, the copillar[5]­arene <b>2a</b> bearing a short 4-hydroxybutyloxy group does not show such a self-assembly behavior to the formation of the self-inclusion monomer and hugging dimers

Synergistic Effects Induced by a Low Dose of Diesel Particulate Extract and Ultraviolet‑A in <i>Caenorhabditis elegans</i>: DNA Damage-Triggered Germ Cell Apoptosis

Diesel exhaust has been classified as a potential carcinogen and is associated with various health effects. A previous study showed that the doses for manifesting the mutagenetic effects of diesel exhaust could be reduced when coexposed with ultraviolet-A (UVA) in a cellular system. However, the mechanisms underlying synergistic effects remain to be clarified, especially in an <i>in vivo</i> system. In the present study, using <i>Caenorhabditis elegans</i> (<i>C. elegans</i>) as an <i>in vivo</i> system we studied the synergistic effects of diesel particulate extract (DPE) plus UVA, and the underlying mechanisms were dissected genetically using related mutants. Our results demonstrated that though coexposure of wild type worms at young adult stage to low doses of DPE (20 μg/mL) plus UVA (0.2, 0.5, and 1.0 J/cm²) did not affect worm development (mitotic germ cells and brood size), it resulted in a significant induction of germ cell death. Using the strain of <i>hus-1::gfp</i>, distinct foci of HUS-1::GFP was observed in proliferating germ cells, indicating the DNA damage after worms were treated with DPE plus UVA. Moreover, the induction of germ cell death by DPE plus UVA was alleviated in single-gene loss-of-function mutations of core apoptotic, checkpoint HUS-1, CEP-1/p53, and MAPK dependent signaling pathways. Using a reactive oxygen species (ROS) probe, it was found that the production of ROS in worms coexposed to DPE plus UVA increased in a time-dependent manner. In addition, employing a singlet oxygen (¹O₂) trapping probe, 2,2,6,6-tetramethyl-4-piperidone, coupled with electron spin resonance analysis, we demonstrated the increased ¹O₂ production in worms coexposed to DPE plus UVA. These results indicated that UVA could enhance the apoptotic induction of DPE at low doses through a DNA damage-triggered pathway and that the production of ROS, especially ¹O₂, played a pivotal role in initiating the synergistic process