Negative Can Be Positive: Signed Graph Neural Networks for Recommendation

Most of the existing GNN-based recommender system models focus on learning users’ personalized preferences from these (explicit/implicit) positive feedback to achieve personalized recommendations. However, in the real-world recommender system, the users’ feedback behavior also includes negative feedback behavior (e.g., click dislike button), which also reflects users’ personalized preferences. How to utilize negative feedback is a challenging research problem. In this paper, we first qualitatively and quantitatively analyze the three kinds of negative feedback that widely existed in real-world recommender systems and investigate the role of negative feedback in recommender systems. We found that it is different from what we expected — not all negative items are ranked low, and some negative items are even ranked high in the overall items. Then, we propose a novel Signed Graph Neural Network Recommendation model (SiGRec) to encode the users’ negative feedback behavior. Our SiGRec can learn positive and negative embeddings of users and items via positive and negative graph neural network encoders, respectively. Besides, we also define a new Sign Cosine (SiC) loss function to adaptively mine the information of negative feedback for different types of negative feedback. Extensive experiments on four datasets demonstrate the proposed model outperforms several existing models. Specifically, on the Zhihu dataset, SiGRec outperforms the unsigned GNN model (i.e., LightGCN), 27.58% 29.81%, and 31.21% in P@20, R@20, and nDCG@20, respectively. We hope our work can open the door to further exploring the negative feedback in recommendations.     

GAFM: A Knowledge Graph Completion Method Based on Graph Attention Faded Mechanism

Although the Knowledge Graph (KG) has been successfully applied to various applications, there is still a large amount of incomplete knowledge in the KG. This study proposes a Knowledge Graph Completion (KGC) method based on the Graph Attention Faded Mechanism (GAFM) to solve the problem of incomplete knowledge in KG. GAFM introduces a graph attention network that incorporates the information in multi-hop neighborhood nodes to embed the target entities into low dimensional space. To generate a more expressive entity representation, GAFM gives different weights to the neighborhood nodes of the target entity by adjusting the attention value of neighborhood nodes according to the variation of the path length. The attention value is adjusted by the attention faded coefficient, which decreases with the increase of the distance between the neighborhood node and the target entity. Then, considering that the capsule network has the ability to fit features, GAFM introduces the capsule network as the decoder to extract feature information from triple representations. To verify the effectiveness of the proposed method, we conduct a series of comparative experiments on public datasets (WN18RR and FB15k-237). Experimental results show that the proposed method outperforms baseline methods. The Hits@10 metric is improved by 8% compared with the second-place KBGAT method.     

Personalized Recommendation via Multi-dimensional Meta-paths Temporal Graph Probabilistic Spreading

Since meta-paths have the innate ability to capture rich structure and semantic information, meta-path-based recommendations have gained tremendous attention in recent years. However, how to composite these multi-dimensional meta-paths? How to characterize their dynamic characteristics? How to automatically learn their priority and importance to capture users' diverse and personalized preferences at the user-level granularity? These issues are pivotal yet challenging for improving both the performance and the interpretability of recommendations. To address these challenges, we propose a personalized recommendation method via Multi-Dimensional Meta-Paths Temporal Graph Probabilistic Spreading (MD-MP-TGPS). Specifically, we first construct temporal multi-dimensional graphs with full consideration of the interest drift of users, obsolescence and popularity of items, and dynamic update of interaction behavior data. Then we propose a dimension-free temporal graph probabilistic spreading framework via multi-dimensional meta-paths. Moreover, to automatically learn the priority and importance of these multi-dimensional meta-paths at the user-level granularity, we propose two boosting strategies for personalized recommendation. Finally, we conduct comprehensive experiments on two real-world datasets and the experimental results show that the proposed MD-MP-TGPS method outperforms the compared state-of-the-art methods in such performance indicators as precision, recall, F1-score, hamming distance, intra-list diversity and popularity in terms of accuracy, diversity, and novelty.     

社会书签与网络信息推荐服务

本文论述了社会书签的基本原理与特点，介绍了社会书签服务的一些实例，并讨论了其在网络信息推荐服务中的主要应用。     

社会网络视角下的信息推荐

源于社会学的社会网络理论注重分析社会网络中的联系，而不是该网络中的节点属性。从社会网络理论的视角出发分析并给出了实现信息推荐的几幸中方法及其相应原理，并简要分析与社会网络理论相关的其它推荐系统。     

A Convolutional Attention Network for Unifying General and Sequential Recommenders

General recommenders and sequential recommenders are two modeling paradigms of recommender. The main focus of a general recommender is to identify long-term user preferences, while the user’s sequential behaviors are ignored and sequential recommenders try to capture short-term user preferences by exploring item-to-item relations, failing to consider general user preferences. Recently, better performance improvement is reported by combining these two types of recommenders. However, most of the previous works typically treat each item separately and assume that each user–item interaction in a sequence is independent. This may be a too simplistic assumption, since there may be a particular purpose behind buying the successive item in a sequence. In fact, a user makes a decision through two sequential processes, i.e., start shopping with a particular intention and then select a specific item which satisfies her/his preferences under this intention. Moreover, different users usually have different purposes and preferences, and the same user may have various intentions. Thus, different users may click on the same items with an attention on a different purpose. Therefore, a user’s behavior pattern is not completely exploited in most of the current methods and they neglect the distinction between users’ purposes and their preferences. To alleviate those problems, we propose a novel method named, CAN, which takes both users’ purposes and preferences into account for the next-item recommendation. We propose to use Purpose-Specific Attention Unit (PSAU) in order to discriminately learn the representations of user purpose and preference. The experimental results on real-world datasets demonstrate the advantages of our approach over the state-of-the-art methods.     

基于混合图的在线社交网络个性化推荐系统研究

文章提出一种基于混合图的在线社交网络个性化推荐系统,将用户社会关系网络和社会化行为融入信息推荐.该系统包括模型构建、推荐流程和算法设计三部分.首先构建了用户资源混合图,并讨论了混合图的构建方法及权重设置,再在构建的混合图上采用重启动随机游走进行顶点间相似度计算,得到个性化推荐列表,进行推荐.     

面向网络用户的个性化推荐服务实现

从网络行为的社会学特征出发,将用户行为数据进行行为-需求定性映射,从而对表达不同需求层次的行为数据进行深度需求挖掘.据此构建基于用户网络行为的推荐服务模型,探讨了基于应用该模型基础上个性化推荐服务的实现.     

人工神经网络在图论中的应用

人工神经网络与图论之间有密切的联系.本文把人工神经网络应用于图论的问题求解中,利用Hopfield网络对图的着色、图的最大独立集和最大团进行求解,构造了各自的能量函数,进而得出网络的运行方程.     

社会化推荐中基于对分网络的用户偏好预测

本文首先阐述了对分网络算法在社会化推荐中的应用,然后分析了社会化推荐的运作机理,构建了社会化推荐模型,最后从相似用户集构建、基于对分网络的用户偏好预测和算法评价3个方面,进行了基于对分网络的用户偏好预测实现研究。评价表明对分网络方法对用户偏好预测的效果较好。     

Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network

AimIn a pilot study to improve detection of malignant lesions in breast mammograms, we aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural networks: (i) graph convolutional network (GCN); and (ii) convolutional neural network (CNN).MethodWe utilised a standard 8-layer CNN, then integrated two improvement techniques: (i) batch normalization (BN) and (ii) dropout (DO). Finally, we utilized rank-based stochastic pooling (RSP) to substitute the traditional max pooling. This resulted in BDR-CNN, which is a combination of CNN, BN, DO, and RSP. This BDR-CNN was hybridized with a two-layer GCN, and yielded our BDR-CNN-GCN model which was then utilized for analysis of breast mammograms as a 14-way data augmentation method.ResultsAs proof of concept, we ran our BDR-CNN-GCN algorithm 10 times on the breast mini-MIAS dataset (containing 322 mammographic images), achieving a sensitivity of 96.20±2.90%, a specificity of 96.00±2.31% and an accuracy of 96.10±1.60%.ConclusionOur BDR-CNN-GCN showed improved performance compared to five proposed neural network models and 15 state-of-the-art breast cancer detection approaches, proving to be an effective method for data augmentation and improved detection of malignant breast masses.     

Taste环境下基于对分网络的社会化推荐引擎构建

协同推荐中相似度计算方法和用户兴趣预测方法的选择,是推荐性能优劣的关键。本文首先分析了社会化推荐的运作机理,构建了基于Taste的社会化推荐模型,阐述了模型实现的关键方法,在此基础上,探讨了Taste环境下基于对分网络推荐算法的社会化推荐引擎构建方法,并利用电影数据进行了实现研究,表明基于对分网络的社会化推荐具有较好的性能。     

复杂网络理论在图书馆个性化推荐服务中的应用

本文将复杂网络理论引入图书馆个性化信息服务的实际问题中,探讨在复杂网络理论下个性化信息推荐服务的模型.在图书馆个性化服务领域引入复杂网络理论,以复杂性科学的高度研究图书馆的个性化推荐服务,可以进一步提升图书馆个性化推荐服务的质量.     

重视网络文化 建设文化强国

网络文化是文化发展的新形态,建设文化强国不能忽视网络文化这一新领域。加强网络文化建设对传播社会主义核心价值观、社会主义先进文化、中华传统文化,提升中华文化国际影响力具有重要的现实意义。网络文化建设要以社会主义核心价值观为指导,用优秀文化产品充实网络文化内容,扩大网络文化的覆盖面,提高网民素质,加强网络人才队伍建设。     

基于舆情大数据的网民关注度转移模型研究

[目的/意义]基于舆情大数据研究网民关注度转移模型,能够深入解读大数据环境下网络舆情事件的竞争效应,可以为网络舆情治理提供参考依据。[方法/过程]定性分析大数据环境下网络舆情事件竞争效应以及网民关注度转移机理,基于微分方程组构建网民关注度转移模型,通过研究模型特性和数值仿真,理解两个舆情事件之间网民关注度转移的定量关系以及未来趋势,并给出估计模型参数的方法。[结论/结果]经过理论建模和实证分析得出本文构建的网民关注度转移模型是可行的,尤其是可以通过舆情数据分析确定多个舆情事件的竞争结果以及网民关注度转移的关键节点,为进一步研究网民关注度转移趋势预测问题提供模型基础。     

简约HJ神经网络在图书馆个性化信息推荐中的应用

通过仿真实验得出,运用简约HJ神经网络原理可以从我们所收集查找到的信息中分离出和读者需求信息最接近的信息,这样可以大大降低我们个性化信息推荐的盲目性和低效性,从而更进一步提升我们图书馆的个性化信息服务质量。     

旅游景区网络关注度时空分布特征分析

2011年6月,中国旅游总评榜组委会公布了中国旅游业第一份百强景区排行榜,这100个景区代表了中国旅游景区的最高水平,也是游客最青睐的景区,因此成为网络空间最受关注的旅游搜索对象。文章选取前47个景区为研究对象,基于百度指数搜索平台,获取了47个景区2010年1月1日到12月31日逐日网络空间关注度数据,对其周内分布和季节性分布进行实证分析。研究发现旅游景区网络关注度在时空分布上具有以下特征:①以周时段考察呈现波动态势,表现为平日高、周末低,周三最高,周六最低,南方景区网络空间关注度高于北方景区;②季节性表现为4月和9月高的"双峰"特征,旺季是4、6、7、8、9、10、11月,平季是3、5、12月,淡季是1月和2月,与现实旅游流相比具有旺季长,淡季短的特点。北方景区的波谷出现在6月,而南方景区出现在5月,南方景区较北方景区季节性波动更大;③黄金周呈现明显"井喷现象",网络关注度均偏向黄金周前期,且"十一"期间较"五一"期间偏向更明显。     

HGNN: Hyperedge-based graph neural network for MOOC Course Recommendation

Previous studies on Course Recommendation (CR) mainly focus on investigating the sequential relationships among courses (RNN is applied) and fail to learn the similarity relationships among learners. Moreover, existing RNN-based methods can only model courses’ short-term sequential patterns due to the inherent shortcomings of RNNs. In light of the above issues, we develop a hyperedge-based graph neural network, namely HGNN, for CR. Specifically, (1) to model the relationships among learners, we treat learners (i.e., hyperedges) as the sets of courses in a hypergraph, and convert the task of learning learners’ representations to induce the embeddings for hyperedges, where a hyperedge-based graph attention network is further proposed. (2) To simultaneously consider courses’ long-term and short-term sequential relationships, we first construct a course sequential graph across learners, and learn courses’ representations via a modified graph attention network. Then, we feed the learned representations into a GRU-based sequence encoder to infer their short-term patterns, and deem the last hidden state as the learned sequence-level learner embedding. After that, we obtain the learners’ final representations by a product pooling operation to retain features from different latent spaces, and optimize a cross-entropy loss to make recommendations. To evaluate our proposed solution HGNN, we conduct extensive experiments on two real-world datasets, XuetangX and MovieLens. We conduct experiments on MovieLens to prove the extensibility of our solution on other collections. From the experimental results, we can find that HGNN evidently outperforms other recent CR methods on both datasets, achieving 11.96% on P@20, 16.01% on NDCG@20, and 27.62% on MRR@20 on XuetangX, demonstrating the effectiveness of studying CR in a hypergraph, and the importance of considering both long-term and short-term sequential patterns of courses.