Recommendation Method for Contrastive Enhancement of Neighborhood Information

Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.

Modeling Price-Aware Session-Based Recommendation Based on Graph Neural Network

Session-based Recommendation(SBR)aims to accurately recom-mend a list of items to users based on anonymous historical session sequences.Existing methods for SBR suffer from several limitations:SBR based on Graph Neural Network often has information loss when constructing session graphs;Inadequate consideration is given to influencing factors,such as item price,and users’dynamic interest evolution is not taken into account.A new session recommendation model called Price-aware Session-based Recommendation(PASBR)is proposed to address these limitations.PASBR constructs session graphs by information lossless approaches to fully encode the original session information,then introduces item price as a new factor and models users’price tolerance for various items to influence users’preferences.In addition,PASBR proposes a new method to encode user intent at the item category level and tries to capture the dynamic interest of users over time.Finally,PASBR fuses the multi-perspective features to generate the global representation of users and make a prediction.Specifically,the intent,the short-term and long-term interests,and the dynamic interests of a user are combined.Experiments on two real-world datasets show that PASBR can outperform representative baselines for SBR.

SGT:Session-based Recommendation with GRU and Transformer

Session-based recommendation aims to predict user preferences based on anonymous behavior sequences.Recent research on session-based recommendation systems has mainly focused on utilizing attention mechanisms on sequential patterns,which has achieved significant results.However,most existing studies only consider individual items in a session and do not extract information from continuous items,which can easily lead to the loss of information on item transition relationships.Therefore,this paper proposes a session-based recommendation algorithm(SGT)based on Gated Recurrent Unit(GRU)and Transformer,which captures user interests by learning continuous items in the current session and utilizes all item transitions on sessions in a more refined way.By combining short-term sessions and long-term behavior,user dynamic preferences are captured.Extensive experiments were conducted on three session-based recommendation datasets,and compared to the baseline methods,both the recall rate Recall@20 and the mean reciprocal rank MRR@20 of the SGT algorithm were improved,demonstrating the effectiveness of the SGT method.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Intention-aware for Session-based Recommendation with Multi-channel Network

Session-based recommendation predicts the user’s next action by exploring the item dependencies in an anonymous session.Most of the existing methods are based on the assumption that each session has a single intention,items irrelevant to the single intention will be regarded as noises.However,in real-life scenarios,sessions often contain multiple intentions.This paper designs a multi-channel Intention-aware Recurrent Unit(TARU)network to further mining these noises.The multi-channel TARU explicitly group items into the different channels by filtering items irrelevant to the current intention with the intention control unit.Furthermore,we propose to use the attention mechanism to adaptively generate an effective representation of the session’s final preference for the recommendation.The experimental results on two real-world datasets denote that our method performs well in session recommendation tasks and achieves improvement against several baselines on the general metrics.

A Novel Popularity Extraction Method Applied in Session-Based Recommendation

Popularity plays a significant role in the recommendation system. Traditional popularity is only defined as a static ratio or metric (e.g., a ratio of users who have rated the item and the box office of a movie) regardless of the previous trends of this ratio or metric and the attribute diversity of items. To solve this problem and reach accurate popularity, we creatively propose to extract the popularity of an item according to the Proportional Integral Differential (PID) idea. Specifically, Integral (I) integrates a physical quantity over a time window, which agrees with the fact that determining the attributes of items also requires a long-term observation. The Differential (D) emphasizes an incremental change of a physical quantity over time, which coincidentally caters to a trend. Moreover, in the Session-Based Recommendation (SBR) community, many methods extract session interests without considering the impact of popularity on interest, leading to suboptimal recommendation results. To further improve recommendation performance, we propose a novel strategy that leverages popularity to enhance the session interest (popularity-aware interest). The proposed popularity by PID is further used to construct the popularity-aware interest, which consistently improves the recommendation performance of the main models in the SBR community. For STAMP, SRGNN, GCSAN, and TAGNN, on Yoochoose1/64, the metric P@20 is relatively improved by 0.93%, 1.84%, 2.02%, and 2.53%, respectively, and MRR@20 is relatively improved by 3.74%, 1.23%, 2.72%, and 3.48%, respectively. On Movieslen-1m, the relative improvements of P@20 are 7.41%, 15.52%, 8.20%, and 20.12%, respectively, and that of MRR@20 are 2.34%, 12.41%, 20.34%, and 19.21%, respectively.

Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing

The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively promoted the intelligent development of these aspects.Although the IoT has gradually grown in recent years,there are still many problems that need to be overcome in terms of technology,management,cost,policy,and security.We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data.To avoid the leakage and loss of various user data,this paper developed a hybrid algorithm of kernel function and random perturbation method based on the algorithm of non-negative matrix factorization,which realizes personalized recommendation and solves the problem of user privacy data protection in the process of personalized recommendation.Compared to non-negative matrix factorization privacy-preserving algorithm,the new algorithm does not need to know the detailed information of the data,only need to know the connection between each data;and the new algorithm can process the data points with negative characteristics.Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of preserving users’personal privacy.

Problems and Recommendations for Rural Statistics and Survey Methods

With constant deepening of the reform and opening-up,national economic system has changed from planned economy to market economy,and rural survey and statistics remain in a difficult transition period. In this period,China needs transforming original statistical mode according to market economic system. All levels of government should report and submit a lot and increasing statistical information. Besides,in this period,townships,villages and counties are faced with old and new conflicts. These conflicts perplex implementation of rural statistics and survey and development of rural statistical undertaking,and also cause researches and thinking of reform of rural statistical and survey methods.

A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces project attribute fuzzy matrix,measures the project relevance through fuzzy clustering method,and classifies all project attributes.Then,the weight of the project relevance is introduced in the user similarity calculation,so that the nearest neighbor search is more accurate.In the prediction scoring section,considering the change of user interest with time,it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation,so that the newer evaluation information in the system has a relatively large weight.The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.

BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and practitioners.Different from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely studied.Early efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of behaviors.However,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of behaviors.However,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of behaviors.The limitation hinders the development of HSBR and results in unsatisfactory performance.As a response,we propose a novel behavior-aware graph neural network(BGNN)for HSBR.Our BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a session.Moreover,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified way.We then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our BGNN.An exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can easily and effectively be extended to multibehavior scenarios.

Predicting the CME arrival time based on the recommendation algorithm

CME is one of the important events in the sun-earth system as it can induce geomagnetic disturbance and an associated space environment effect.It is of special significance to predict whether CME will reach the Earth and when it will arrive.In this paper,we firstly built a new multiple association list for 215 different events with 18 characteristics including CME features,eruption region coordinates and solar wind parameters.Based on the CME list,we designed a novel model based on the principle of the recommendation algorithm to predict the arrival time of CMEs.According to the two commonly used calculation methods in the recommendation system,cosine distance and Euclidean distance,a controlled trial was carried out respectively.Every feature has been found to have its own appropriate weight.The error analysis indicates the result using the Euclidean distance similarity is much better than that using cosine distance similarity.The mean absolute error and root mean square error of test data in the Euclidean distance are 11.78 and 13.77 h,close to the average level of other CME models issued in the CME scoreboard,which verifies the effectiveness of the recommendation algorithm.This work gives a new endeavor using the recommendation algorithm,and is expected to induce other applications in space weather prediction.

Interpretation and Classification of P-Series Recommendations in ITU-R

As ITU-R Recommendations is widely implemented for countries all over the world, the role and status of ITU-R Recommendations are increasingly prominent in the field of radio engineering. ITU and ITU-R Study Groups are summarized. Furthermore, the operating mode of the third study group, and the input documents are interpreted in detail. Lastly, from both wireless system design and electromagnetic compatibility analysis perspective, all of 79 P-series Recommendations are analyzed and classified, and the main contents of each Recommendation are summarized. The above research promote P-series Recommendations are widely used in China.

Self-supervised graph learning with target-adaptive masking for session-based recommendation

Session-based recommendation aims to predict the next item based on a user’s limited interactions within a short period.Existing approaches use mainly recurrent neural networks(RNNs)or graph neural networks(GNNs)to model the sequential patterns or the transition relationships between items.However,such models either ignore the over-smoothing issue of GNNs,or directly use cross-entropy loss with a softmax layer for model optimization,which easily results in the over-fitting problem.To tackle the above issues,we propose a self-supervised graph learning with target-adaptive masking(SGL-TM)method.Specifically,we first construct a global graph based on all involved sessions and subsequently capture the self-supervised signals from the global connections between items,which helps supervise the model in generating accurate representations of items in the ongoing session.After that,we calculate the main supervised loss by comparing the ground truth with the predicted scores of items adjusted by our designed target-adaptive masking module.Finally,we combine the main supervised component with the auxiliary self-supervision module to obtain the final loss for optimizing the model parameters.Extensive experimental results from two benchmark datasets,Gowalla and Diginetica,indicate that SGL-TM can outperform state-of-the-art baselines in terms of Recall@20 and MRR@20,especially in short sessions.

BA-GNN: Behavior-aware graph neural network for session-based recommendation

Session-based recommendation is a popular research topic that aims to predict users’next possible interactive item by exploiting anonymous sessions.The existing studies mainly focus on making predictions by considering users’single interactive behavior.Some recent efforts have been made to exploit multiple interactive behaviors,but they generally ignore the influences of different interactive behaviors and the noise in interactive sequences.To address these problems,we propose a behavior-aware graph neural network for session-based recommendation.First,different interactive sequences are modeled as directed graphs.Thus,the item representations are learned via graph neural networks.Then,a sparse self-attention module is designed to remove the noise in behavior sequences.Finally,the representations of different behavior sequences are aggregated with the gating mechanism to obtain the session representations.Experimental results on two public datasets show that our proposed method outperforms all competitive baselines.The source code is available at the website of GitHub.

基于实测数据的高炉煤气单位热值含碳量推荐值的修正方法

钢铁工业碳排放核算方法依据GB/T 32151.5-2015《温室气体排放核算与报告要求第5部分:钢铁生产企业》所提供的排放因子法,单位热值含碳量(C_(C))作为表征可燃物质在单位热值下碳元素含量的指标,是排放因子法计算碳排放量的重要影响因子。前人对于钢铁工业碳排放研究主要侧重于各种核算方法之间的差异性,而对于单位热值含碳量推荐值准确性鲜有研究。为研究可燃物质单位热值含碳量对于碳排放计算的影响,选择钢铁工业炼铁工序副产气体高炉煤气作为研究对象,基于某钢厂高炉煤气各项指标实测数据,计算得出不同组分(CO和CO_(2))含量下高炉煤气C_(C)实测值,分析不同组分含量高炉煤气低位发热量实测值、全碳组分C_(C)实测值、燃烧过程C_(C)实测值等实测参数变化。结合排放因子法广泛应用的现状,基于C_(C)实测值波动情况,提出3种将C_(C)推荐值修正为燃烧过程C_(C)的方法,在各自适用条件修正值与实测值的偏差均小于5%;最后,用该钢厂高炉炼铁工序2021年有关统计数据,针对高炉煤气C_(C)推荐值修正前后高炉本体碳排放计算结果进行对比分析,修正前高炉本体每吨铁的碳排放量-0.19 t,修正后高炉本体每吨铁的碳排放量0.415 t,修正结果与真实碳排放情况较吻合。研究结果表明,在计算钢铁工业高炉本体二氧化碳排放时,使用实测均值、修正系数w(CO)[w(CO)+w(CO_(2))]和w(CO)—C_(com)拟合曲线修正高炉煤气单位热值含碳量,计算结果与实际相符。

结合图卷积神经网络和集成方法的推荐系统恶意攻击检测

推荐系统已被广泛应用于电子商务、社交媒体、信息分享等大多数互联网平台中,有效解决了信息过载问题。然而,这些平台面向所有互联网用户开放,导致不法用户利用系统设计缺陷通过恶意干扰、蓄意攻击等行为非法操纵评分数据,进而影响推荐结果,严重危害推荐服务的安全性。现有的检测方法大多都是基于从评级数据中提取的人工构建特征进行的托攻击检测,难以适应更复杂的共同访问注入攻击,并且人工构建特征费时且区分能力不足,同时攻击行为规模远远小于正常行为,给传统检测方法带来了不平衡数据问题。因此,文中提出堆叠多层图卷积神经网络端到端学习用户和项目之间的多阶交互行为信息得到用户嵌入和项目嵌入,将其作为攻击检测特征,以卷积神经网络作为基分类器实现深度行为特征提取,结合集成方法检测攻击。在真实数据集上的实验结果表明,与流行的推荐系统恶意攻击检测方法相比,所提方法对共同访问注入攻击行为有较好的检测效果并在一定程度上克服了不平衡数据的难题。

农用地土壤重金属来源解析与健康风险空间分异特征研究——以重庆市巫山县笃坪乡为例

【目的】探明长江上游地区农用地土壤重金属来源及生态风险状况,进行人体健康风险空间分析,有助于提出有效的土壤重金属污染防治建议。【方法】以重庆市巫山县笃坪乡为研究对象,采集表层土壤样品(深度0~20 cm)45件,基于主成分分析/绝对主成分分数(PCA/APCS)受体模型进行土壤重金属(Cd、Hg、Pb、As和Cr)来源定量分析,通过人体健康风险评价模型进行人体健康风险评价,利用地统计法进行人体健康风险空间分析,得出不同区域的风险等级及影响因素,并针对性地提出风险管控建议。【结果】(1)研究区土壤Cd、Hg、Pb、As、Cr平均含量分别是重庆市土壤背景值的10.67、3.18、1.03、2.05、4.22倍,土壤重金属含量存在显著异常;(2)土壤综合环境质量优先保护类、安全利用类和严格管控类点位占比分别为2.23%、44.44%和53.33%,主要影响因子为Cd和Cr,土壤以酸性为主,对农产品质量安全及土壤生态环境的风险较高;(3)土壤Cd含量主要受到农业活动和成土母质的影响,贡献率分别为58.55%和30.30%,土壤Hg和Cr含量主要受到成土母质的影响,贡献率分别为71.55%和75.41%,土壤As含量主要受到农业活动的影响,贡献率为63.12%,土壤Pb含量主要受到道路交通的影响,贡献率为90.90%;(4)成人非致癌健康风险指数HI>1的点位占比为6.67%,总体健康风险较低,高风险区集中在研究区北部和中部地区,主要影响因子为Cr;农业活动与道路交通贡献率的高值区主要分布在研究区北部。【结论】建议研究区加强对农业投入品的监测,积极开展有机肥代替等化肥减量措施,推广使用电动农用车,减少由于农业活动对土壤重金属的输入。

滨海软土地基二次堆载预压固结沉降研究

滨海软土往往工程性质较差,一次堆载预压通常达不到设计要求而需要进行二次堆载预压。本文通过现场原位试验和室内试验,对二次堆载预压2年后的地基软土开展物理力学性质研究,并将试验获得的土体参数用于规范法和数值模拟,对地基未来20年的沉降进行预测。结果表明,二次堆载预压2年后,部分软土层力学参数提升显著,但淤泥层力学参数未见明显改善,地基承载力未达到设计要求,后续仍有较大的沉降变形。同时,对比规范法和研究开始前18个月沉降规律发现,经过二次堆载预压后的地基,数值模拟采用弹塑性模型能更准确地预测后续沉降,二次堆载预压20年后地基最大的沉降量可达1 m,位于12号钻孔位置处,其次是18号钻孔位置处,沉降量为0.9 m,10号钻孔位置处沉降量为0.8 m,并且沉降主要集中在淤泥层中。

基于LDA-MURE模型的背景音乐自适应推荐方法

用户的情绪状态不同,需要的背景音乐也不同,因此提出基于LDA-MURE模型的背景音乐自适应推荐方法。提取背景音乐的音频特征和社会化标签,通过Fisher线性判别分析方法融合上述数据的特征,结合投影变换方法获得不同类别背景音乐的类内离散度和类间离散度。通过现代心理学分析人类情绪的节律周期变化,在此基础上判断用户当前的情绪状态。最后在LDA模型的基础上构建LDA-MURE模型,为用户推荐不同类别的背景音乐。实验结果表明,所提方法的MEA指标值较低、P@N指标值较高、用户满意度较高。

心脏神经症中医诊疗专家共识

心脏神经症是一种心血管症状与自主神经兴奋表现共同出现的神经症,中医药治疗心脏神经症效果明确,临床应用广泛,但临床诊疗并未达成一致的共识,对临床诊疗的有效性和规范性造成了一定的限制。因此中国中医药研究促进会心身医学专业委员会组织全国范围内30余位心血管或心身医学领域专家制定心脏神经症中医诊疗专家共识,推荐心脏神经症对应中医病名为卑惵,其核心症状为心悸、心前区疼痛、胸闷、气短、失眠、心烦、抑郁、焦虑、恐惧、情绪难以控制等,其证候主要包括肝郁脾虚证、肝火扰心证、气滞血瘀证、痰火扰心证、心胆气虚证、心肝阴虚证。中医药治疗心脏神经症效果确切、不良反应少,明确辨证论治纲要后对临床诊疗有较高的指导作用。