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Currently, popularity based recommendation has become a research hotspot. The use of popularity considerably improves the recommendation effects, while the Matthew effect caused by popularity bias has also garnered extensive attention among researchers. Some researchers consider combining both aspects to produce hybrid popularity based recommendation. Adopting the concept of popularity, a unified representation of popularity, popularity bias, and hybrid popularity is provided in this paper. Firstly, the background of popularity in the field of recommendation is introduced. Then, based on different perspectives, a comprehensive survey on popularity-enhanced recommendation methods, popularity debias recommendation methods, and hybrid popularity based recommendation methods is provided. Each type of method is further subdivided in specific subtasks of modeling or concrete strategies. The representative models of each method are introduced and analyzed, and their advantages and limitations are evaluated. The mechanisms and applicable scenarios of each method are also summarized in detail. Furthermore, the commonly used datasets, performance evaluation indicators and baseline are introduced. A comparative analysis of the representative methods performance is also listed. Finally, some opinions on the trends of popularity based recommendation are presented. An outlook on the technical difficulties and hotspots for future development from multiple perspectives is analyzed and predicted.

Recommendation systems are designed to recommend personalized content to improve user experience. At present, the recommendation systems still face some challenges such as poor interpretability, cold start problem and serialized recommendation modeling. Recently, the knowledge graph (KG) containing a large amount of semantic and structural information has been widely used in a variety of different recommendation tasks to alleviate the above problems. This paper systematically reviews the innovative applications of knowledge graph embedding (KGE) in different recommendation tasks. It first summarizes three common recommendation tasks and four applying goals of knowledge graph embedding. Then, it generalizes four types of knowledge graph embedding methods according to specific technologies, including traditional embedding method, embedding propagation method, heterogeneous graph embedding method and graph neural network based method. It further elaborates on the applying characteristics and strategies of the above four methods in different recommendation tasks, and evaluates advantages and limitations of each method. Also, it conducts qualitative and quantitative analysis of the associations and differences of four methods from multiple aspects. Finally, it puts forward some views on the development trend of applying knowledge graph embedding for recommendation systems, and proposes several noteworthy development directions in the future from multiple perspectives.

Objective: Winter-over expeditioners in Antarctica are challenged by various environmental and psycho-social stress factors, which may induce psychophysiological changes. The autonomic nervous system (ANS) plays a crucial role in the adaptation process under stress. However, the relationship between ANS activity and the mood states of expeditioners remains largely unexplored. This study aims to uncover the pattern of ANS adjustment under extreme Antarctic environments and provide new insights into the correlations between ANS activity and mood state changes, which may provide scientific data for medical interventions. Methods: Fourteen expeditioners at Zhongshan Station participated in this study. The study was conducted during four representative periods: pre-Antarctica, Antarctica-1 (pre-winter), Antarctica-2 (winter), and Antarctica-3 (summer). The heart rate variability (HRV) of the expeditioners was continuously measured for 24 hours to evaluate ANS activity. Plasma levels of catecholamines were tested by ELISA. Mood states were assessed by the Profile of Mood States (POMS) scale. Results: HRV analysis showed a disturbance of ANS during winter and summer periods. For frequency domain parameters, very low frequency (VLF), low frequency (LF), high frequency (HF), and total power (TP) significantly increased during the second half of the mission. Especially, LF/HF ratio decreased during summer, indicating the predominance of vagal tone. Results of the time domain analysis showed increased heart rate variability during the austral winter and summer. Plasma epinephrine (E) significantly increased during residence in Antarctica. Compared with pre-Antarctica, the vigor, depression, and anger scores of the expeditioners decreased significantly during the austral summer. Notably, the depression score showed a moderate positive correlation with LF/HF, while weak negative correlations with other HRV indicators, including TP, VLF, and LF. Anger score showed a moderate positive correlation with LF/HF and weak negative correlations with the average normal-to-normal (NN) interval, and the root mean square of differences between adjacent RR intervals (RMSSD). Plasma E level weakly correlated with the average NN interval. Conclusion: Prolonged residence in Antarctica increased the ANS activities and shifted the cardiac autonomic modulation towards vagal predominance. The alteration of HRV correlated with mood states and plasma epinephrine levels. [ABSTRACT FROM AUTHOR]

News recommendation (NR) can effectively alleviate the overload of news information, and it is an important way to obtain news information for users. Deep learning (DL) has become a mainstream technology to promote the development of NR in recent years, and the effect of news recommendation has been significantly improved, which is widely concerned by researchers. In this paper, the methods of deep learning-based news recommendation (DNR) are classified, analyzed and summarized. In the research of NR, modeling users or news are two key tasks. According to different strategies of modeling users or news, the news recommendation methods based on deep learning are divided into three types: “two-stage” method, “fusion” method and “collaboration” method. Each type of method is further subdivided in terms of sub-tasks or the data organization structure based on. The representative models of each method are introduced and analyzed, and their advantages and limitations are evaluated. The characteristics, advantages and disadvantages of each type of methods are also summarized in detail. Furthermore, the commonly used datasets, baseline and performance evaluation indicators are introduced. Finally, the possible future research directions and development trends in this field are analyzed and predicted.

People spend up to 90% of their time inside buildings, making indoor air quality an extremely important factor affecting public health and building design. Due to the inherent ability to absorb/filter pollutants, plants present a promising method for improving indoor air quality. In recent decades, many studies have quantified plants’ effectiveness in removing indoor air pollutants using both chamber and field methods. This paper presents a review working covering these studies and discusses the differences between chamber and field studies, in terms of study methods and results. Through a meta-analysis of 41 chamber studies and 16 field studies, the effectiveness of 182 species in removing 25 pollutants has been estimated. From this work, a larger proportion of significant results were observed in chamber studies (88%), comparing to field studies (65%). Additionally, comparable studies revealed greater removal effectiveness of plants in chamber studies. These discrepancies could be attributed to many factors, such as the size and the airtightness of experimental setup, ventilation, gas exposure scheme, and environmental conditions. It is envisaged that these findings will help reduce the gap between chamber studies and field studies, and provide guidance for the future use of plants in buildings to improve indoor air quality.