用户-兴趣点耦合关系的兴趣点推荐方法

在基于位置的社交网络(LBSNs)中,如何利用用户和兴趣点的属性(或特征)之间的耦合关系,为用户做出准确的兴趣点推荐是当前的研究热点.现有的矩阵分解推荐方法利用用户对兴趣点的评分进行推荐,但评级矩阵通常非常稀疏,并且没有考虑用户和兴趣点在各自属性方面的耦合关系.本文提出了一种基于深度神经网络的兴趣点推荐框架,首先采用K...     

Transitive node similarity: predicting and recommending links in signed social networks

Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content. OSNs recommend new friends to registered users based on local features of the graph (i.e., based on the number of common friends that two users share). However, OSNs do not exploit the whole structure of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we define a basic node similarity measure that captures effectively local graph features (i.e., by measuring proximity between nodes). We exploit global graph features (i.e., by weighting paths that connect two nodes) introducing transitive node similarity. We also derive variants of our method that apply to different types of networks (directed/undirected and signed/unsigned). We perform extensive experimental comparison of the proposed method against existing recommendation algorithms using synthetic and real data sets (Facebook, Hi5 and Epinions). Our experimental results show that our FriendTNS algorithm outperforms other approaches in terms of accuracy and it is also time efficient. Finally, we show that a significant accuracy improvement can be gained by using information about both positive and negative edges.     

融合重叠社区正则化及隐式反馈的协同过滤方法

针对目前推荐系统存在的数据稀疏和冷启动等问题,提出了一种融合重叠社区正则化及隐式反馈的协同过滤方法(OCRIF),该方法不仅考虑了用户在社交网络中的社区结构,而且将用户评分信息与社交信息的隐式反馈融入推荐模型之中.此外,由于网络表示学习可以有效学习节点在社交网络的全局结构上的近邻信息,提出了一种网络表示学习增强的OCR...     

Finding Diachronic Like‐Minded Users

User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, just to name a few, they do not necessarily succeed at pulling like‐minded users that share the same interests together. Therefore, researchers have explored the topical similarity of social content to build like‐minded communities of users. In this article, following the topic‐based approaches, we are interested in identifying communities of users that share similar topical interests with similar temporal behavior. More specifically, we tackle the problem of identifying temporal (diachronic) topic‐based communities, i.e., communities of users who have a similar temporal inclination toward emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user toward emerging topics. Further, our modeling is completely agnostic to the underlying topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph‐based and two latent Dirichlet allocation‐based methods. Through our experiments on Twitter data, we demonstrate the effectiveness of our proposed temporal topic‐based community detection method in the context of news recommendation, user prediction, and document timestamp prediction applications, compared with the nontemporal as well as the state‐of‐the‐art temporal approaches.     

联合社区和影响节点的通用可扩展的链接预测

针对相似度预测算法无法同时嵌入局部和全局信息并提高运行速度等问题,融合社区发现和影响节点识别技术提出一个通用可扩展的链接预测模型。对网络进行社区划分,分别计算局部共邻节点的社区参与度和全局影响力得分,集成到统一的相似度框架中。为验证算法的有效性和可扩展性,给出在加权和无权下多个局部密集结构和影响节点识别指标的定义。在真实数据集上的实验结果表明,提出方法可快速实现通用可扩展性的预测任务,结果也普遍优于基准算法。     

融合社交信息的局部潜在空间推荐方法

随着社交网络的发展,越来越多的研究利用社交信息来改进传统推荐算法的性能,然而现有的推荐算法大多忽略了用户兴趣的多样化,未考虑用户在不同社交维度中关心的层面不同,导致推荐质量较差.为了解决这个问题,提出了一种同时考虑全局潜在因子和不同子集特定潜在因子的推荐方法LSFS,使得推荐过程既考虑了用户共享偏好又考虑了用户在不同子集中的特定偏好.考虑到参与到不同社交维度的用户对不同的项目感兴趣,首先根据用户的社交关系将用户划分到不同的子集中;其次通过截断奇异值分解技术建模用户对项目的评分,其中全局潜在因子捕获用户共享的层面,而不同用户子集的特定潜在因子捕获用户关心的特定层面;最后,结合全局与局部潜在因子预测用户对未评分项目的评分.实验结果表明该方法可行且有效.     

Joint user knowledge and matrix factorization for recommender systems

Currently, most of the existing recommendation methods treat social network users equally, which assume that the effect of recommendation on a user is decided by the user’s own preferences and social influence. However, a user’s own knowledge in a field has not been considered. In other words, to what extent does a user accept recommendations in social networks need to consider the user’s own knowledge or expertise in the field. In this paper, we propose a novel matrix factorization recommendation algorithm based on integrating social network information such as trust relationships, rating information of users and users’ own knowledge. Specifically, since we cannot directly measure a user’s knowledge in the field, we first use a user’s status in a social network to indicate a user’s knowledge in a field, and users’ status is inferred from the distributions of users’ ratings and followers across fields or the structure of domain-specific social network. Then, we model the final rating of decision-making as a linear combination of the user’s own preferences, social influence and user’s own knowledge. Experimental results on real world data sets show that our proposed approach generally outperforms the state-of-the-art recommendation algorithms that do not consider the knowledge level difference between the users.     

基于图神经网络的异构信任推荐算法

传统基于图神经网络的社交推荐算法通过加强用户和项目特征的学习提升预测精度,但随着用户数据日益稀疏和社交关系趋于复杂,推荐质量提升缓慢。为挖掘用户和项目的潜在关联关系,提出一种结合图神经网络的异构信任推荐算法（GraphTrust）。在显式信任关系的基础上获取用户的潜在好友,根据动态影响力传播模型将图神经网络中的节点和边进行分类,通过不同类型的边在不同节点间进行影响力传播扩散,捕捉隐藏在高阶网络结构中的影响力扩散特征,并使用户和项目的潜在特征随着影响力传播过程达到平衡状态,最终将用户交互的项目特征作为辅助特征与用户特征聚合进行评分预测。在Yelp和Flickr数据集上的实验结果表明,当潜在特征维数为64时,GraphTrust算法相比于DiffNet++算法的命中率和归一化折损累计增益分别提升了13.2%、22.2%和20.4%、25.5%,在一定程度上提高了推荐过程的可解释性和预测精度,并且缓解了数据稀疏问题。     

Improving music recommendation by incorporating social influence

In the past decades, a large number of music pieces are uploaded to the Internet every day through social networks, such as Last.fm, Spotify and YouTube, that concentrates on music and videos. We have been witnessing an ever-increasing amount of music data. At the same time, with the huge amount of online music data, users are facing an everyday struggle to obtain their interested music pieces. To solve this problem, music search and recommendation systems are helpful for users to find their favorite content from a huge repository of music. However, social influence, which contains rich information about similar interests between users and users’ frequent correlation actions, has been largely ignored in previous music recommender systems. In this work, we explore the effects of social influence on developing effective music recommender systems and focus on the problem of social influence aware music recommendation, which aims at recommending a list of music tracks for a target user. To exploit social influence in social influence aware music recommendation, we first construct a heterogeneous social network, propose a novel meta path-based similarity measure called WPC, and denote the framework of similarity measure in this network. As a step further, we use the topological potential approach to mine social influence in heterogeneous networks. Finally, in order to improve music recommendation by incorporating social influence, we present a factor graphic model based on social influence. Our experimental results on one real world dataset verify that our proposed approach outperforms current state-of-the-art music recommendation methods substantially.

     

基于用户邻域和主题的新颖性Web社区推荐方法

社区推荐从海量社区中为用户过滤出有价值的社区,变得越来越重要.新颖性推荐逐渐得到关注,因为单纯追求准确度的推荐结果存在局限性.已有新颖性推荐方法不适用于社区推荐,因其无法处理Web社区特性,包括社区成员用户通过交互形成的关系网络以及社区主题.提出了一种新颖性社区推荐方法NovelRec,向用户推荐其有潜在兴趣但不知道的社区,旨在拓展用户视野和推动社区发展.NovelRec基于用户交互网络中的邻域关系,利用用户之间在主题上的关联,计算候选社区对用户的准确度;根据用户与社区在邻域和主题上的关联,提出一种用户社区距离度量方式,并利用该距离计算候选社区的新颖度.在此基础上,NovelRec最终进行新颖性社区推荐,并兼顾推荐结果的准确性.真实数据集上的对比实验结果表明,NovelRec方法在新颖性上优于现有方法,同时能够保证推荐结果的准确性.     

融合社交关系和局部地理因素的兴趣点推荐

兴趣点（Point-Of-Interest,POI）推荐是基于位置社交网络（Location-Based Social Network,LBSN）中一项重要的个性化服务,可以帮助用户发现其感兴趣的[POI],提高信息服务质量。针对[POI]推荐中存在的数据稀疏性问题,提出一种融合社交关系和局部地理因素的[POI]推荐算法。根据社交关系中用户间的共同签到和距离关系度量用户相似性,并基于用户的协同过滤方法构建社交影响模型。为每个用户划分一个局部活动区域,通过对区域内[POIs]间的签到相关性分析,建立局部地理因素影响模型。基于加权矩阵分解挖掘用户自身偏好,并融合社交关系和局部地理因素进行[POI]推荐。实验表明,所提出的[POI]推荐算法相比其他方法具有更高的准确率和召回率,能够有效缓解数据稀疏性问题,提高推荐质量。     

基于对抗采样的社交推荐算法

推荐系统利用用户的历史记录、物品的基础信息等数据进行建模来捕获用户的偏好,有效缓解了信息过载等问题,虽然其已应用广泛,但整个推荐领域面临的挑战却依旧存在,其中数据稀疏这一问题对于推荐性能有举足轻重的影响。近年来,大量研究表明基于社交信息的推荐算法能够有效缓解数据稀疏问题,但它们也仍然存在一定的局限。线上的社交网络是非常稀疏的,并且线上社交网络中的“朋友”通常包括同学、同事、亲戚等,因此,拥有显式朋友关系的用户不一定拥有相似的偏好,即直接利用显式朋友的兴趣偏好进行推荐会存在噪声问题。此外,大部分基于隐式反馈的算法通常直接对用户没有交互过的物品进行随机采样,然后将其作为用户实际交互过的物品的负样本来优化模型,然而用户没有交互过的物品并不代表用户不喜欢,这种粗粒度的采样策略忽略了用户的真实偏好,同样也带来了一定程度的噪声。生成对抗网络（GANs）因其在训练中捕获复杂数据分布的能力以及强大的鲁棒性被广泛应用到推荐系统中,为了减弱上述噪声问题带来的影响,本文基于生成对抗网络提出了一种细粒度的对抗采样推荐模型（ASGAN）,包括一个生成器和判别器。其中,生成器首先利用图表示学习技术初始化社交网络,接着为用户生成一个与其偏好相似的朋友,然后再从该朋友喜欢的物品集中同时生成该用户喜欢的物品和用户不喜欢的物品。判别器则尽可能区分出用户实际交互过的物品和生成器生成的两类物品。随着对抗训练的进行,生成器能更有效地进行社交朋友采样和物品采样,而判别器能够良好地捕获用户的真实偏好分布。最后,在三个公开的真实数据集上与现有的六个工作进行对比,实验结果证明：ASGAN拥有更好的推荐性能,通过重构社交网络和细粒度采样有效缓解了社交信息和物品采样策略带来的噪声问题。     

CSD: A multi-user similarity metric for community recommendation in online social networks

Communities are basic components in networks. As a promising social application, community recommendation selects a few items (e.g., movies and books) to recommend to a group of users. It usually achieves higher recommendation precision if the users share more interests; whereas, in plenty of communities (e.g., families, work groups), the users often share few. With billions of communities in online social networks, quickly selecting the communities where the members are similar in interests is a prerequisite for community recommendation. To this end, we propose an easy-to-compute metric, Community Similarity Degree (CSD), to estimate the degree of interest similarity among multiple users in a community. Based on 3460 emulated Facebook communities, we conduct extensive empirical studies to reveal the characteristics of CSD and validate the effectiveness of CSD. In particular, we demonstrate that selecting communities with larger CSD can achieve higher recommendation precision. In addition, we verify the computation efficiency of CSD: it costs less than 1 hour to calculate CSD for over 1 million of communities. Finally, we draw insights about feasible extensions to the definition of CSD, and point out the practical uses of CSD in a variety of applications other than community recommendation.     

A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

Due to the popularity of group activities in social media, group recommendation becomes increasingly significant. It aims to pursue a list of preferred items for a target group. Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users. However, these methods may suffer from data sparsity problem. Except for the interaction between groups and users, there also exist other interactions that may enrich group representation, such as the interaction between groups and items. Such interactions, which take place in the range of a group, form a local view of a certain group. In addition to local information, groups with common interests may also show similar tastes on items. Therefore, group representation can be conducted according to the similarity among groups, which forms a global view of a certain group. In this paper, we propose a novel global and local information fusion neural network (GLIF) model for group recommendation. In GLIF, an attentive neural network (ANN) activates rich interactions among groups, users and items with respect to forming a group′s local representation. Moreover, our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups. Then, it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation. Finally, group recommendation is conducted under neural collaborative filtering (NCF) framework. Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

     

Incorporating metapath interaction on heterogeneous information network for social recommendation

Heterogeneous information network (HIN) has recently been widely adopted to describe complex graph structure in recommendation systems, proving its effectiveness in modeling complex graph data. Although existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths, they have the following major limitations. Existing works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths, which are not expressive enough to capture more complicated dependency relationships involved on the metapath. Besides, the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately considered. To tackle these limitations, we propose a novel social recommendation model MPISR, which models MetaPath Interaction for Social Recommendation on heterogeneous information network. Specifically, our model first learns the initial node representation through a pretraining module, and then identifies potential social friends and item relations based on their similarity to construct a unified HIN. We then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different metapaths. Extensive experiments on five real datasets demonstrate the effectiveness of our method.     

基于时序逆影响的随机游走推荐算法*

相似性计算是协同过滤推荐的关键步骤,针对传统相似性计算认为相似关系是对等的且没有考虑消费顺序和时间间隔的问题,提出了基于时序逆影响的随机游走推荐算法。首先,基于用户时序关联图提出一种新的称为时序逆影响的相似性度量,利用随机游走得到了目标用户近邻集合;其次,利用随机游走在项目时序关联图上进一步改进推荐的多样性和覆盖率。它不但认为用户间相似是不对称的,考虑了用户消费项目的顺序和时间间隔,获得了用户全局的直接和间接近邻,而且考虑了项目间的时序逆影响。通过在真实数据集上的大量试验结果表明,与其他随机游走方法相比,不但能提高推荐性能、缓解数据稀疏,而且通过提高多样性和覆盖率解决了过拟合的问题。     

“With a little help from new friends”: Boosting information cascades in social networks based on link injection

We investigate information cascades in the context of viral marketing applications. Recent research has identified that communities in social networks may hinder cascades. To overcome this problem, we propose a novel method for injecting social links in a social network, aiming at boosting the spread of information cascades. Unlike the proposed approach, existing link prediction methods do not consider the optimization of information cascades as an explicit objective. In our proposed method, the injected links are being predicted in a collaborative-filtering fashion, based on factorizing the adjacency matrix that represents the structure of the social network. Our method controls the number of injected links to avoid an “aggressive” injection scheme that may compromise the experience of users. We evaluate the performance of the proposed method by examining real data sets from social networks and several additional factors. Our results indicate that the proposed scheme can boost information cascades in social networks and can operate as a “people recommendations” strategy complementary to currently applied methods that are based on the number of common neighbors (e.g., “friend of friend”) or on the similarity of user profiles.     

基于三度影响力的社交好友推荐机制

针对社交网络中的好友推荐问题,提出了一种基于三度影响力理论的好友推荐算法。社交网络用户节点间的联系除了共同好友外,还存在其他不同长度的连通关系。该算法不再局限于仅以用户间共同好友的数量作为好友推荐的主要依据,而是在此基础上引入三度影响力理论进一步拓展关系连接,即把用户间距离三度以内的强连接用户都考虑进来,并通过为不同距离长度的连通关系分配相应的权重,实现好友关系强度的计算,来进行推荐。通过在新浪微博和Facebook社交网站上的实验结果表明,该算法比仅依据用户间共同好友数量的推荐算法在查准率和查全率上分别提高了约5%和0.8%,显著提升了社交平台好友推荐的效果,从而为社交平台改进推荐机制,以进一步增强用户体验提供了理论支撑。     

结合用户组群和隐性信任的概率矩阵分解推荐

研究表明在社会网络推荐中添加明确的社会信任明显提高了评分的预测精度,但现实生活中很难得到用户之间明确的信任评分。之前已有学者研究并提出了信任度量方法来计算和预测用户之间的相互作用及信任评分。提出了一种基于Hellinger距离的社会信任关系提取方法,通过描述二分网络中一侧节点的f散度来进行用户相似度计算。然后结合用户分组信息,将提取的隐式社会关系加入改进的概率矩阵分解中,提出一种新的基于用户组群和隐性社会关系的概率矩阵分解算法（CH-PMF）。实验结果表明,提出的模型与应用实际用户明确表示的信任分数推荐结果表现几乎相同,且在无法提取到明确信任数据时,CH-PMF有着比其他传统算法更好的推荐效果。     

融合社交网络和兴趣的正则化矩阵分解推荐模型

针对目前用户偏好数据和社交关系数据十分稀疏的问题,以及用户可能更加喜欢朋友推荐的商品而不喜欢非朋友推荐的商品这样一个事实,提出了一种结合社交网络和用户间的兴趣偏好相似度的正则化矩阵分解推荐算法,首先针对社交关系数据稀疏问题,利用网络的全局和局部拓扑特性挖掘出用户间的信任和不信任关系矩阵,然后定义了一种改进的用户间的兴趣偏好相似度计算方法,最后在矩阵分解的过程中将信任矩阵、不信任矩阵以及兴趣相关性综合起来为用户作出推荐。实验表明该方法优于主要的正则化推荐方法,与基本的矩阵分解模型（SocialMF）、SoRec、TrustMF、CTRPMF、RecSSN算法相比,算法在均方根误差（RMSE）和平均绝对误差（MAE）上分别减小了1.1%~9.5%和2%~10.1%,取得了较好的推荐效果。