Topic-Centric Explanations for News Recommendation

News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests. Recent methods have demonstrated considerable success in terms of recommendation performance. However, the lack of explanation for these recommendations can lead to mistrust among users and lack of acceptance of recommendations. To address this issue, we propose a new explainable news model to construct a topic-aware explainable recommendation approach that can both accurately identify relevant articles and explain why they have been recommended, using information from associated topics. Additionally, our model incorporates two coherence metrics applied to assess topic quality, providing measure of the interpretability of these explanations. The results of our experiments on the MIND dataset indicate that the proposed explainable NRS outperforms several other baseline systems, while it is also capable of producing interpretable topics compared to those generated by a classical LDA topic model. Furthermore, we present a case study through a real-world example showcasing the usefulness of our NRS for generating explanations.Comment: 20 pages, submitted to a journa

CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition

Audio-visual person recognition (AVPR) has received extensive attention. However, most datasets used for AVPR research so far are collected in constrained environments, and thus cannot reflect the true performance of AVPR systems in real-world scenarios. To meet the request for research on AVPR in unconstrained conditions, this paper presents a multi-genre AVPR dataset collected `in the wild', named CN-Celeb-AV. This dataset contains more than 419k video segments from 1,136 persons from public media. In particular, we put more emphasis on two real-world complexities: (1) data in multiple genres; (2) segments with partial information. A comprehensive study was conducted to compare CN-Celeb-AV with two popular public AVPR benchmark datasets, and the results demonstrated that CN-Celeb-AV is more in line with real-world scenarios and can be regarded as a new benchmark dataset for AVPR research. The dataset also involves a development set that can be used to boost the performance of AVPR systems in real-life situations. The dataset is free for researchers and can be downloaded from http://cnceleb.org/.Comment: INTERSPEECH 202

Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations

Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using advanced natural language processing techniques to extract semantic information from rich textual data, employing content-based methods derived from local historical news. However, this approach lacks a global perspective, failing to account for users' hidden motivations and behaviors beyond semantic information. To address this challenge, we propose a novel model called GLORY (Global-LOcal news Recommendation sYstem), which combines global representations learned from other users with local representations to enhance personalized recommendation systems. We accomplish this by constructing a Global-aware Historical News Encoder, which includes a global news graph and employs gated graph neural networks to enrich news representations, thereby fusing historical news representations by a historical news aggregator. Similarly, we extend this approach to a Global Candidate News Encoder, utilizing a global entity graph and a candidate news aggregator to enhance candidate news representation. Evaluation results on two public news datasets demonstrate that our method outperforms existing approaches. Furthermore, our model offers more diverse recommendations.Comment: 10 pages, Recsys 202

NumHTML : numeric-oriented hierarchical transformer model for multi-task financial forecasting

Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learning-based methods are limited in the way that they deal with numeric data; numbers are typically treated as plain-text tokens without taking advantage of their underlying numeric structure. This paper describes a numeric-oriented hierarchical transformer model (NumHTML) to predict stock returns, and financial risk using multi-modal aligned earnings calls data by taking advantage of the different categories of numbers (monetary, temporal, percentages etc.) and their magnitude. We present the results of a comprehensive evaluation of NumHTML against several state-of-the-art baselines using a real-world publicly available dataset. The results indicate that NumHTML significantly outperforms the current state-of-the-art across a variety of evaluation metrics and that it has the potential to offer significant financial gains in a practical trading context

Status of cardiovascular health among adults in a rural area of Northwest China: Results from a cross-sectional study.

The aim of this study was to assess the status of cardiovascular health among a rural population in Northwest China and to determine the associated factors for cardiovascular health.A population-based cross-sectional study was conducted in the rural areas of Hanzhong in Northwest China. Interview, physical examination, and fasting blood glucose and lipid measurements were completed for 2693 adults. The construct of cardiovascular health and the definitions of cardiovascular health metrics proposed by the American Heart Association were used to assess cardiovascular health. The proportions of subjects with cardiovascular health metrics were calculated, adjusting for age and sex. The multiple logistic regression model was used to evaluate the association between ideal cardiovascular health and its associated factors.Only 0.5% (0.0% in men vs 0.9% in women, P = 0.002) of the participants had ideal cardiovascular health, whereas 33.8% (18.0% in men vs 50.0% in women, P < 0.001) and 65.7% (82.0% in men vs 49.1% in women, P < 0.001) of the participants had intermediate and poor cardiovascular health, respectively. The prevalence of poor cardiovascular health increased with increasing age (P < 0.001 for trend). Participants fulfilled, on average, 4.4 (95% confidence interval: 4.2-4.7) of the ideal cardiovascular health metrics. Also, 22.2% of the participants presented with 3 or fewer ideal metrics. Only 19.4% of the participants presented with 6 or more ideal metrics. 24.1% of the participants had all 4 ideal health factors, but only 1.1% of the participants had all 4 ideal health behaviors. Women were more likely to have ideal cardiovascular health, whereas adults aged 35 years or over and those who had a family history of hypertension were less likely to have ideal cardiovascular health.The prevalence of ideal cardiovascular health was extremely low among the rural population in Northwest China. Most adults, especially men and the elderly, had a poor cardiovascular health status. To improve cardiovascular health among the rural population, efforts, especially lifestyle improvements, education and interventions to make healthier food choices, reduce salt intake, increase physical activities, and cease smoking, will be required at the individual, population, and social levels

Global land surface temperature influenced by vegetation cover and PM2.5 from 2001 to 2016

Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 �g/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72�N and 48�S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature

An improved model to estimate trapping parameters in polymeric materials and its application on normal and aged low-density polyethylenes

Trapping parameters can be considered as one of the important attributes to describe polymeric materials. In the present paper, a more accurate charge dynamics model has been developed, which takes account of charge dynamics in both volts-on and off stage into simulation. By fitting with measured charge data with the highest R-square value, trapping parameters together with injection barrier of both normal and aged low-density polyethylene samples were estimated using the improved model. The results show that, after long-term ageing process, the injection barriers of both electrons and holes is lowered, overall trap depth is shallower, and trap density becomes much greater. Additionally, the changes in parameters for electrons are more sensitive than those of holes after ageing

Lot-sizing and pricing decisions for perishable products under three-echelon supply chains when demand depends on price and stock-age

[[abstract]]In economics, a demand curve is almost always downward-sloping, reflecting the willingness of consumers to purchase more of the commodity at lower price levels. In addition, the demand for seasonal products (such as fashion apparels, beverages etc.) or perishable goods (such as meat and seafood, dairy products, fruit and vegetables, pharmaceutical products, and chemicals) decreases over time. Hence, demand is a function of price and stock-age. With large business transactions, a seller usually demands a down payment (i.e., an advance payment) to ensure that the buyer is making a serious offer. Conversely, a buyer frequently requests to hold a fraction of total purchase cost until the business transaction is completed and satisfactory (i.e., a credit payment). As a result, a combination of advance, cash, and credit (ACC) payments is commonly used in business transactions. This paper develops a supplier–retailer–customer chain in which the retailer receives an upstream ACC payment from the supplier while in return offers a down-stream cash-credit (some in cash and the remainder in credit) payment to customers, the demand is influenced by the combined effect of selling price and stock age, and the deterioration rate is time-varying. The retailer must determine optimal unit price and replenishment time to maximize the present value of total profit, which is strictly concave in selling price and strictly pseudo-concave in replenishment time. Finally, a sensitivity analysis is performed, and several managerial insights are obtained. For instance, an increase in the fraction of advance payment forces the retailer to raise selling price.[[notice]]補正完

Pricing and lot-sizing policies for perishable products with advance-cash-credit payments by a discounted cash-flow analysis

[[abstract]]A contractor often requests a customer pay an advance payment when signing a contract to install a new roof. A cash payment to cover the contractor's materials cost is then required upon delivery of the materials to do the job. Then, the contractor grants the customer a credit payment to pay the remainder of the total cost after the work is completed and satisfactory. Hence, an advance-cash-credit (ACC) payment scheme is commonly used in real world business transactions. This paper develops a supplier-retailer-customer chain in which the retailer receives an upstream ACC payment from the supplier while in return offers a downstream cash-credit (some in cash and the rest in credit) payment to customers. Additionally, today's health-conscious consumers judge product freshness through its expiration date because the freshness of a perishable product degrades with time. As a result, the demand for perishable products is influenced by the combined effect of selling price and product freshness linked to expiration date. Taking time value of money into consideration, then an inventory model by using a discounted cash-flow analysis is developed. Furthermore, the present value of total annual profit is demonstrated that is strictly concave in unit price and strictly pseudo-concave in replenishment time, which simplifies the search for the global solution to a local maximum. Finally, a sensitivity analysis is conducted and several managerial insights are obtained.[[notice]]補正完