Co_(3)O_(4)as an efficient passive NO_(x) adsorber for emission control during cold-start of diesel engines

The Co_(3)O_(4)nanoparticles,dominated by a catalytically active(110)lattice plane,were synthesized as a low-temperature NO_(x) adsorbent to control the cold start emissions from vehicles.These nanoparticles boast a substantial quantity of active chemisorbed oxygen and lattice oxygen,which exhibited a NO_(x) uptake capacity commensurate with Pd/SSZ-13 at 100℃.The primary NO_(x) release temperature falls within a temperature range of 200-350℃,making it perfectly suitable for diesel engines.The characterization results demonstrate that chemisorbed oxygen facilitate nitro/nitrites intermediates formation,contributing to the NO_(x) storage at 100℃,while the nitrites begin to decompose within the 150-200℃range.Fortunately,lattice oxygen likely becomes involved in the activation of nitrites into more stable nitrate within this particular temperature range.The concurrent processes of nitrites decomposition and its conversion to nitrates results in a minimal NO_(x) release between the temperatures of 150-200℃.The nitrate formed via lattice oxygen mainly induces the NO_(x) to be released as NO_(2) within a temperature range of 200-350℃,which is advantageous in enhancing the NO_(x) activity of downstream NH_(3)-SCR catalysts,by boosting the fast SCR reaction pathway.Thanks to its low cost,considerable NO_(x) absorption capacity,and optimal release temperature,Co_(3)O_(4)demonstrates potential as an effective material for passive NO_(x) adsorber applications.

Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model has very goodpredictive performance when predicting missing edges of isolated nodes.

An Incremental Graph Pattern Matching Based Dynamic Cold-Start Recommendation Method

In order to give accurate recommendations for cold-start user, researchers use social network to find similar users. These efforts assume that cold-start user’s social relationships are static. However social relationships of cold-start user may change as time pass by. In order to give accurate and timely in manner recommendations for cold-start user, it is need to update social relationship continuously. In this paper, we proposed an incremental graph pattern matching based dynamic cold-start recommendation method (IGPMDCR), which updates similar users for cold-start user based on topology of social network, and gives recommendations based on the latest similar users’ records. The experimental results show that, IGPMDCR could give accurate and timely in manner recommendations for cold-start user.

新闻推荐系统中用户冷启动问题的研究

提出利用用户上下文信息,解决新闻推荐系统中用户冷启动问题的方法.通过已有用户对于新闻的点击浏览记录,提取其在不同环境中的上下文信息,并利用兴趣分类记录构建决策树分类模型.新用户到达时,提取此用户在当前环境中所带有的上下文信息并与决策树模型进行匹配,以此预测新用户的新闻浏览兴趣,并将新闻主题与用户兴趣进行匹配,进而完成新闻推荐.实验结果表明,本文提出的基于用户上下文信息的方法能够有效缓解新闻推荐系统中用户冷启动问题,用户满意度明显提高,新闻推荐结果更为人性化.

基于用户概要扩展的协同过滤算法

针对协同过滤算法中的新用户冷启动问题,提出了基于用户概要扩展的协同过滤算法(EUPCF)。算法采用一种新的加权朴素贝叶斯方法对新用户的概要进行局部扩展,然后使用扩展后的概要为新用户进行预测推荐,为预测项目提供更多近邻项目。新的加权朴素贝叶斯方法为每个条件属性独立计算后验概率,避免了传统方法中联合分布先验概率对数据稀疏度的敏感性问题,提高了扩展的准确度。Movie Lens数据集实验表明,新算法拥有良好的预测准确度,同时,不会对推荐的实时性产生较大影响。

推荐系统冷启动问题解决策略研究

推荐系统利用机器学习技术进行信息过滤,快速准确地定位用户需要的信息,并且能够预测用户对目标项目的喜好程度。由于新用户与新项目的存在,传统的推荐系统在缺少数据信息的情况下面临着冷启动问题的挑战,导致系统无法为用户产生准确的推荐。分析冷启动产生的原因,阐述解决冷启动问题的意义,从是否考虑冷启动类型等方面对目前推荐系统冷启动问题的研究成果进行分类总结,并尝试给出冷启动问题未来的研究重点与难点。目前较为普遍的处理方式是将多种数据源与多种推荐方法进行混合使用,从而提高系统推荐的准确度与效率,但是仍然存在着如在收集用户各类信息的同时如何保护个人隐私、如何建立推荐系统的效用评价等难点问题。

融合用户相似度与评分信息的协同过滤算法

推荐系统利用机器学习的技术进行信息过滤,准确地定位用户需要的信息,并且能够预测用户对目标项目的喜好程度。但是由于新用户和新项目的存在,传统的协同过滤推荐系统面临着冷启动问题的挑战。为了解决协同过滤推荐系统中用户冷启动问题,设计了融合用户相似度与评分信息的协同过滤算法(SR-CF)。该算法用基于人口统计学的推荐算法找出用户基本信息之间的相似度,再根据最速下降法对用户评分矩阵进行更新,从而产生对目标用户的推荐。基于Moive Lens公开数据集的实验结果表明,所设计的算法在保证推荐准确率的同时提高了推荐的覆盖率,能有效解决用户冷启动问题。

高校新读者图书个性化推荐服务研究

[目的/意义]针对阻碍高校智慧图书馆对新读者进行图书个性化推荐的用户冷启动问题,提出一种面向新读者的高校图书馆个性化推荐方法,为智慧型高校图书馆开展图书个性化推荐服务、提高新读者借阅率提供切实可行的方案。[方法/过程]以北华大学图书馆借阅流通大数据进行数据挖掘,得出属性相似的新读者和已有读者具有相似借阅偏好的结论。然后,通过奇异值分解解决数据稀疏问题,采用基于欧氏距离的蚁群算法对新读者与已有读者聚类,搭建了新读者和已有读者之间关系的桥梁。最后将已有读者借阅的图书采取Top-N算法对新读者推荐。[结果/结论]以2017级读者为实验对象,选取了3个学院的44名读者,用所提出的算法进行了实验检验。实验结果表明新算法推荐效果显著,操作简单可行,为后续个性化推荐工作奠定了基础。

基于互信息的鲁棒跨域推荐系统

由于大量新用户和新产品的出现,跨域推荐系统已经成为解决推荐系统冷启动问题的关键。然而,现有的跨域推荐系统都假设其训练数据中不存在任何的错误标注,但是在现实情况下,该假设很难得到满足,这就导致了跨域推荐系统在相当多的真实推荐场景下的表现很难令人满意。为了减少现实情况下错误标注对跨域推荐系统的影响,提高真实推荐场景下跨域推荐系统推荐结果的准确性,本文提出了一种基于互信息的鲁棒跨域推荐系统,该推荐系统由域分离网络和互信息鲁棒风险两个模块构成。域分离网络模块很好地解决了源域与目标域差异的问题;在互信息鲁棒风险模块中,提出了一个基于互信息的风险函数来过滤掉数据中的错误标注,使用该风险函数所训练出的跨域推荐系统可以很好地处理训练数据中存在的错误信息,使跨域推荐系统能更好地应用在各种真实的推荐场景下。本文采用对比试验的方法,在真实的数据集上将所提出的方法与几种现有的推荐方法进行了比较,试验表明,现有的推荐方法在现实情况下很难不受到错误标注的影响,而本文提出的方法很好地应对了错误标注的影响,具有更优越的性能。

Applying memetic algorithm-based clustering to recommender system with high sparsity problem

A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.

Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services.Thus,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service.Most of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality recommendations.Inspired by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.The proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel.Next,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features.Finally,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks.We conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation metrics.Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance.Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems.

A Well-Built Hybrid Recommender System for Agricultural Products in Benue State of Nigeria

Benue State of Nigeria is tagged the Food Basket of the country due to its heavy production of many classes of food. Situated in the North Central Geo-Political area of the country, its food production ranges from root crops, fruits to cereals. Recommender systems (RSs) allow users to access products of interest, given a plethora of interest on the Internet. Recommendation techniques are content-based and collaborative filtering. Recommender systems based on collaborative filtering outshines content-based systems in the quality of their recommendations, but suffers from the cold start problem, i.e., not being able to recommend items that have few or no ratings. On the other hand, content-based recommender systems are able to recommend both old and new items but with low recommendation quality in relation to the user’s preference. This work combines collaborative filtering and content based recommendation into one system and presents experimental results obtained from a web and mobile application used in the simulation. The work solves the problem of serendipity associated with content based (RS) as well as the problem of ramp-up associated with collaborative filtering. The results indicate that the quality of recommendation is promising and is competitive with collaborative technique recommending items that have been seen before and also effective at recommending cold-start products.

Graph-enhanced neural interactive collaborative filtering

To improve the training efficiency and recommendation accuracy in cold-start interactive recommendation systems,a new graph structure called item similarity graph is proposed on the basis of real data from a public dataset.The proposed graph is built from collaborative interactions and a deep reinforcement learning-based graph-enhanced neural interactive collaborative filtering(GE-ICF)model.The GE-ICF framework is developed with a deep reinforcement learning framework and comprises an embedding propagation layer designed with graph neural networks.Extensive experiments are conducted to investigate the efficiency of the proposed graph structure and the superiority of the proposed GE-ICF framework.Results show that in cold-start interactive recommendation systems,the proposed item similarity graph performs well in data relationship modeling,with the training efficiency showing significant improvement.The proposed GE-ICF framework also demonstrates superiority in decision modeling,thereby increasing the recommendation accuracy remarkably.

Thermal Modeling of a Novel Heated Tip Injector for Otto Cycle Engines Powered by Ethanol

This work presents a thermal modeling of a new cold-start system technology designed for Otto cycle combustion based on the electromagnetic heating principle. Firstly, the paper presents a state-of-the-art review and presents the context of automobile industry where heated injectors are necessary. The novel method of electromagnetic heating principle to solve the cold-start problem is still in the development phase and it enables engine starting at low temperatures in vehicles powered by ethanol or flex-fuel vehicles (FFV). This new system technology should be available as an alternative to replace the existing system. Currently, the cold-start system uses an auxiliary gasoline tank, which brings some inconvenience for the user. Secondly, the aim was also to create a physical model that takes into consideration all the parameters involved on the heating process such as power heating and average heat transfer coefficient. The study is based on the lumped system theory to model the ethanol heating process. From the analysis, two ordinary differential equations arise, which allowed an analytical solution. Particularly, an ethanol heating curve inside the injector was obtained, an important parameter in the process. Comparison with experimental data from other authors is also provided. Finally, a sensitivity analysis of controlling parameters such as heating power and heat transfer coefficient variation. The paper is concluded with suggestions for further studies.

Item Cold-Start Recommendation with Personalized Feature Selection

The problem of recommending new items to users(often referred to as item cold-start recommendation)remains a challenge due to the absence of users’past preferences for these items.Item features from side information are typically leveraged to tackle the problem.Existing methods formulate regression methods,taking item features as input and user ratings as output.These methods are confronted with the issue of overfitting when item features are high-dimensional,which greatly impedes the recommendation experience.Availing of high-dimensional item features,in this work,we opt for feature selection to solve the problem of recommending top-N new items.Existing feature selection methods find a common set of features for all users,which fails to differentiate users1 preferences over item features.To personalize feature selection,we propose to select item features discriminately for different users.We study the personalization of feature selection at the level of the user or user group.We fulfill the task by proposing two embedded feature selection models.The process of personalized feature selection filters out the dimensions that are irrelevant to recommendations or unappealing to users.Experimental results on real-life datasets with high-dimensional side information reveal that the proposed method is effective in singling out features that are crucial to top-N recommendation and hence improving performance.

Application of self-adaptive temperature recognition in cold-start of an air-cooled proton exchange membrane fuel cell stack

The Self-adaptive control of the temperature can achieve the start of fuel cell at different operating temperatures, which is very important for the successful cold-start of the air-cooled PEMFC. The temperature distribution characteristics during the cold-start process were analyzed based on adaptive temperature recognition control in this paper. Preheating model and cold-start model were established and the optimal balance between the hot air flow rate and the temperature required to promote a uniform temperature distribution in the stack was explored in the preheating stage. Finally, the non-equilibrium mass transfer, as well as the temperature rise in the catalyst layer and gas diffusion layer with different current densities, were analyzed in the start-up stage. The results indicate that the air-cooled PEMFC stack can be successfully started up at -40 ◦C within 10 min by means of external gas heating. The current density and air velocity have significant impacts on the temperature of aircooled PEMFC stack. Dynamic analysis of air-cooled PEMFCs and real-time monitoring are suitable for machine learning and self-adaptive control to set the operation parameters to achieve successful cold start. Optimize the matching of load current and cathode inlet speed to achieve thermal management in low temperature environment.

推荐系统冷启动问题研究进展

文章从冷启动问题的基本应对策略、冷启动问题的具体应对方法和特定领域的冷启动问题3个方面梳理相关研究,发现对于各类冷启动问题已形成基本应对策略;主要方法包括基于内容和/或协作式过滤的方法、基于辅助数据的方法、基于用户参与的方法等;多媒体信息推荐、标签推荐和跨领域推荐等领域中的冷启动问题值得关注;混合式方法的应用、情境信息与关联数据的应用、基于知识图谱的推荐方法与跨领域推荐方法的应用是需要深入研究的方向。

协同过滤系统中基于种子集评分的新用户冷启动推荐研究

认为建立种子集引导用户评分是解决协同过滤推荐系统新用户冷启动问题的方法之一。尝试将关联度引入种子集的构建策略,提出基于多属性综合评价的种子集策略,并利用公开数据集MovieLens设计实验,模拟推荐系统的新用户环境,对比不同种子集策略的预测准确度和成功率。实验结果表明,在更符合实际推荐系统需求的少量种子集情况下,考虑种子之间的关联性可以改善推荐效果。