In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. However, there is little systematic study on DL-based methods, especially regarding to how to design an effective DL model for sequential recommendation. In this view, this survey focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically,we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequence, summarize the key factors affecting the performance of DL-based models, and conduct corresponding evaluations to demonstrate the effects of these factors. We conclude this survey by systematically outlining future directions and challenges in this field.

Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

. Quantification of emission changes is a prerequisite for the assessment of
control effectiveness in improving air quality. However, the traditional
bottom-up method for characterizing emissions requires detailed
investigation of emissions data (e.g., activity and other emission
parameters) that usually takes months to perform and limits timely
assessments. Here we propose a novel method to address this issue by using a
response model that provides real-time estimation of emission changes based
on air quality observations in combination with emission-concentration
response functions derived from chemical transport modeling. We applied the
new method to quantify the emission changes on the North China Plain (NCP)
due to the COVID-19 pandemic shutdown, which overlapped the Spring Festival (also known as Chinese New Year)
holiday. Results suggest that the anthropogenic emissions of NO2 ,
 SO2 , volatile organic compound (VOC) and primary PM 2.5 on the NCP were reduced by 51 %, 28 %,
67 % and 63 %, respectively, due to the COVID-19 shutdown, indicating
longer and stronger shutdown effects in 2020 compared to the previous Spring
Festival holiday. The reductions of VOC and primary PM 2.5 emissions are
generally effective in reducing O3 and PM 2.5 concentrations.
However, such air quality improvements are largely offset by reductions in
 NOx emissions. NOx emission reductions lead to increases in
 O3 and PM 2.5 concentrations on the NCP due to the strongly VOC-limited
conditions in winter. A strong NH3 -rich condition is also suggested
from the air quality response to the substantial NOx emission
reduction. Well-designed control strategies are recommended based on the air
quality response associated with the unexpected emission changes during the
COVID-19 period. In addition, our results demonstrate that the new
response-based inversion model can well capture emission changes based on
variations in ambient concentrations and thereby illustrate the great
potential for improving the accuracy and efficiency of bottom-up emission
inventory methods.

/pdf/quantifying-the-emission-changes-and-associated-air-quality-4vrewdzkm0.pdf

Quantifying the emission changes and associated air quality impacts during the COVID-19 pandemic on the North China Plain: a response modeling study

Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business KPI (Key Performance Indicator) of conversion rate. Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal, such as views, clicks, adding a product to shop carts and so on. They should be taken into account properly to provide quality recommendation for users. In this work, we contribute a new solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from user multi-behavior data. We develop a neural network model to capture the complicated and multi-type interactions between users and items. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data. Further analysis shows that modeling multiple behaviors is particularly useful for providing recommendation for sparse users that have very few interactions.

Neural Multi-Task Recommendation from Multi-Behavior Data

Personalized news recommendation techniques are widely adopted by many online news feed platforms to target user interests. Learning accurate user interest models is important for news recommendation. Most existing methods for news recommendation rely on implicit feedbacks like click behaviors for inferring user interests and model training. However, click behaviors are implicit feedbacks and usually contain heavy noise. In addition, they cannot help infer complicated user interest such as dislike. Besides, the feed recommendation models trained solely on click behaviors cannot optimize other objectives such as user engagement. In this paper, we present a news feed recommendation method that can exploit various kinds of user feedbacks to enhance both user interest modeling and recommendation model training. In our method we propose a unified user modeling framework to incorporate various explicit and implicit user feedbacks to infer both positive and negative user interests. In addition, we propose a strong-to-weak attention network that uses the representations of stronger feedbacks to distill positive and negative user interests from implicit weak feedbacks for accurate user interest modeling. Besides, we propose a multi-feedback model training framework by jointly training the model in the click, finish and dwell time prediction tasks to learn an engagement-aware feed recommendation model. Extensive experiments on real-world dataset show that our approach can effectively improve the model performance in terms of both news clicks and user engagement.

FeedRec: News Feed Recommendation with Various User Feedbacks.

. The inaccuracy of anthropogenic emission inventories on a
high-resolution scale due to insufficient basic data is one of the major
reasons for the deviation between air quality model and observation results.
A bottom-up approach, which is a typical emission inventory estimation method,
requires a lot of human labor and material resources, whereas a top-down
approach focuses on individual pollutants that can be measured directly as well as
relying heavily on traditional numerical modeling. Lately, the deep neural
network approach has achieved rapid development due to its high efficiency and
nonlinear expression ability. In this study, we proposed a novel method to
model the dual relationship between an emission inventory and pollution
concentrations for emission inventory estimation. Specifically, we utilized a
neural-network-based comprehensive chemical transport model (NN-CTM) to
explore the complex correlation between emission and air pollution. We further
updated the emission inventory based on back-propagating the gradient of the
loss function measuring the deviation between NN-CTM and observations from
surface monitors. We first mimicked the CTM model with neural networks (NNs)
and achieved a relatively good representation of the CTM, with similarity
reaching 95 %. To reduce the gap between the CTM and observations, the NN
model suggests updated emissions of NO x , NH 3 , SO 2 , volatile organic compounds (VOCs)
and primary PM 2.5 changing, on average, by − 1.34 %, − 2.65 %, − 11.66 %,
 − 19.19 % and 3.51 %, respectively, in China for 2015. Such
ratios of NO x and PM 2.5 are even higher ( ∼  10 %) in regions that suffer from large uncertainties in
original emissions, such as Northwest China. The updated emission inventory can improve model
performance and make it closer to observations. The mean absolute error for
NO 2 , SO 2 , O 3 and PM 2.5 concentrations are reduced
significantly (by about 10 %–20 %), indicating the high
feasibility of NN-CTM in terms of significantly improving both the accuracy
of the emission inventory and the performance of the air quality model.

/pdf/exploring-deep-learning-for-air-pollutant-emission-1f3cgendoj.pdf

Exploring deep learning for air pollutant emission estimation

Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-related decision making Here, we demonstrate a novel method that combines deep learning approaches with chemical indicators of pollutant formation to quickly estimate the coefficients of air quality response functions using ambient concentrations of 18 chemical indicators simulated with a comprehensive atmospheric chemical transport model (CTM) By requiring only two CTM simulations for model application, the new method significantly enhances the computational efficiency compared to existing methods that achieve lower accuracy despite requiring 20+ CTM simulations (the benchmark statistical model) Our results demonstrate the utility of deep learning approaches for capturing the nonlinearity of atmospheric chemistry and physics and the prospects of the new method to support effective policymaking in other environment systems

Deep learning for prediction of the air quality response to emission changes

AEGCN: An Autoencoder-Constrained Graph Convolutional Network

Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user’s behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging user’s preferences. Further, although heterogeneous graphs have been applied in different areas, a proper way to construct a heterogeneous graph for interactive news data with an appropriate learning mechanism on it is still desired. To address the above concerns, we propose a graph-based behavior-aware network, which simultaneously considers six different types of behaviors as well as user’s demand on the news diversity. We have three mainsteps. First, we build an interaction behavior graph for multi-level and multi-category data. Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM to learn behavior sequence representations. Third, we introduce core and coritivity features for the behavior graph, which measure the concentration degree of user’s interests. These features affect the trade-off between accuracy and diversity of our personalized recommendation system. Taking these features into account, our system finally achieves recommending news to different users at their different levels of concentration degrees.

The graph-based behavior-aware recommendation for interactive news

In this paper, we propose a heuristic recommendation system for interactive news, called the graph-based broad behavior-aware network (G-BBAN). Different from most of existing work, our network considers six behaviors that may potentially be conducted by users, including unclick, click, like, follow, comment, and share. Further, we introduce the core and coritivity concept from graph theory into the system to measure the concentration degree of interests of each user, which we show can help to improve the performance even further if it's considered. There are three critical steps in our recommendation system. First, we build a structured user-dependent interaction behavior graph for multi-level and multi-category data as a preprocessing step. This graph constructs the data sources and knowledge information which will be used in G-BBAN through representation learning. Second, for each user node on the graph, we calculate its core and coritivity and then add the pair as a new feature associated to this user. According to the definition of core and coritivity, this user-dependent feature provides useful insights into the concentration degree of his/her interests and affects the trade-off between accuracy and diversity of the personalized recommendation. Last, we represent item (news) information by entity semantics and environment semantics; design a multi-channel convolutional neural network called G-CNN to learn the semantic information and an attention-based LSTM to learn user's behavior representation; combine with previous concentration feature and input into another two fully connected layers to finish the classification task. The whole network consists of the final G-BBAN. Through comparing with baselines and several variates of itself, our proposed method shows the superior performance in extensive experiments.

The Graph-based Broad Behavior-Aware Recommendation System for Interactive News

Graphical password (GPW) is one of various passwords used in information communication. The QR code, which is widely used in the current world, is one of GPWs. Topsnut-GPWs are new-type GPWs made by topological structures (also, called graphs) and number theory, but the existing GPWs use pictures/images almost. We design new Topsnut-GPWs by means of a graph labelling, called odd-elegant labelling. The new Topsnut-GPWs will be constructed by Topsnut-GPWs having smaller vertex numbers; in other words, they are compound Topsnut-GPWs such that they are more robust to deciphering attacks. Furthermore, the new Topsnut-GPWs can induce some mathematical problems and conjectures.

/pdf/a-new-type-of-graphical-passwords-based-on-odd-elegant-48nclkckwv.pdf

Mingyuan Ma

Papers

Deep learning for prediction of the air quality response to emission changes

AEGCN: An Autoencoder-Constrained Graph Convolutional Network

The graph-based behavior-aware recommendation for interactive news

The Graph-based Broad Behavior-Aware Recommendation System for Interactive News

A New Type of Graphical Passwords Based on Odd-Elegant Labelled Graphs