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-means算法对兴趣点按地理位置进行聚类,使位置相近的兴趣点聚为一类;然后,构建一个卷积神经网络模型,用来学习用户和兴趣点在各自属性（如用户年龄与兴趣点位置之间）上的显式关联关系;同时,构建另外一个神经网络模型,模拟机器学习中的矩阵分解方法,根据用户的签到行为,深入挖掘用户与兴趣点之间的隐式关联关系。最后,将用户与兴趣点之间的显式和隐式关联关系进行集成,综合表征用户?兴趣点之间的耦合关系,然后将学习到的用户?兴趣点耦合关系输入到一个全连接网络中进行兴趣点推荐。本文所提出的模型在Yelp数据集上进行了评估,实验结果表明该模型在兴趣点推荐方面有较高的推荐准确性。     

基于用户-项目交叉注意力机制的迁移推荐模型

随着互联网技术的发展,个性化的推荐系统得到了广泛应用.但用户数据稀疏与冷启动仍是推荐系统普遍面临的难题.将深度学习与注意力机制相结合,提出基于用户-项目交叉注意力机制的迁移推荐模型.该模型能够充分学习源域数据中用户、物品及评分间的潜在关系,然后初始化目标域神经网络,迁移应用到目标域.为验证算法模型的有效性,在公开数据集...     

基于异构信息网络元路径作张量分解的深度学习推荐系统

过去基于学习用户和物品的表征向量的推荐系统算法在大规模数据中取得了较好的结果。相比早期经典的基于矩阵分解（matrix factorization,MF）的推荐算法,近几年流行的基于深度学习的方法,在稀疏的数据集中具有更好的泛化能力。但许多方法只考虑了二维的评分矩阵信息,或者简单的对各种属性做嵌入表征,而忽略了各种属性之间的内部关系。异构信息网络（heterogeneous information network,HIN）相比同构网络能够存储更加丰富的语义特征。近几年结合异构信息网络与深度学习的推荐系统,通过元路径挖掘关键语义信息的方法成为研究热点。
为了更好地挖掘各种辅助信息与用户喜好的关联性,本文结合张量分解、异构信息网络与深度学习方法,提出了新的模型hin-dcf。首先,基于数据集构建特定场景的异构信息网络;对于某一元路径,根据异构图中的路径信息生成其关联性矩阵。其次,合并不同元路径的关联性矩阵后,得到包含用户、物品、元路径三个维度的张量。接着,通过经典的张量分解算法,将用户、物品、元路径映射到相同维度的隐语义向量空间中。并且将分解得到的隐语义向量作为深度神经网络的输入层的初始化。考虑到不同用户对不同元路径的关联性偏好不同,融入注意力机制,学习不同用户、物品,与不同元路径的偏好权重。在实验部分,该模型在精确度上有效提升,并且更好地应对了数据稀疏的问题。最后提出了未来可能的研究方向。     

基于降噪自编码器的社会化推荐算法

现有的社会化推荐算法未考虑信任用户对目标用户深层的偏好影响。针对这一问题,提出了一种基于深度学习的混合推荐算法,利用降噪自编码器学习用户及其信任用户的评分偏好,使用加权隐藏层来平衡这些表示的重要性,有效建模用户间的潜在偏好交互。在此基础上,通过用户聚类和个性化权重区分不同类的用户受其信任用户的影响程度。在开放数据集上的实验结果表明,该算法优于现有的社会化推荐算法,与主要的推荐算法SoRec、RSTE、SocialMF、TrustMF相比,其平均绝对误差(MAE)和均方根误差(RMSE)显著降低,获得了较好的推荐效果。     

融合评分和社会化标签的两阶段深度推荐方法

当前融合评分和标签的推荐方法对两种数据的挖掘程度有限,且大多数局限在提取浅层的线性特征层面.深度学习技术被成功应用于推荐方法,然而数据的稀疏性导致学习的潜在特征效果不好,因此,提出一种融合评分和社会化标签的两阶段深度推荐方法.首先,利用堆叠降噪自编码器分别从评分和社会化标签中提取用户、项目的潜在特征;其次,将学习的潜在特征进行拼接作为用户、项目完整的潜在特征,并与原始评分相结合构建监督学习数据集;最后,将构建的数据集作为BP神经网络的输入以训练评分预测模型.为降低训练误差,通过联合训练的方式进行参数学习.基于MovieLens、Last.FM数据集的实验表明,该方法与几种基准方法相比有更好的推荐性能.     

社交网络中融合社交关系和语义信息的推荐算法

协同过滤方法广泛应用于推荐,但是数据稀疏成为模型提供高质量推荐的一大障碍.为了解决此问题,文中提出融合社交关系和语义信息的推荐算法,提高协同过滤方法的推荐性能,有机融合稀疏的用户行为记录、项目的社交信息和项目的语义信息.应用矩阵分解技术把行为矩阵和项目社交关系映射到一个低维的特征空间,提供项目社交关系信息分解的显式解释,分析关系信息对用户行为偏好产生的影响.同时,使用社会化因子正则的级联去噪自编码器模型学习项目语义特征,改进传统深度学习模型.在真实腾讯微博和Twitter数据集上的实验表明,文中方法有效提高召回率、准确率和推荐效率.     

Bayesian dual neural networks for recommendation

Most traditional collaborative filtering (CF) methods only use the user-item rating matrix to make recommendations, which usually suffer from cold-start and sparsity problems. To address these problems, on the one hand, some CF methods are proposed to incorporate auxiliary information such as user/item profiles; on the other hand, deep neural networks, which have powerful ability in learning effective representations, have achieved great success in recommender systems. However, these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the weights. Therefore, they maybe lack of calibrated probabilistic predictions and make overly confident decisions. To this end, we propose a new Bayesian dual neural network framework, named BDNet, to incorporate auxiliary information for recommendation. Specifically, we design two neural networks, one is to learn a common low dimensional space for users and items from the rating matrix, and another one is to project the attributes of users and items into another shared latent space. After that, the outputs of these two neural networks are combined to produce the final prediction. Furthermore, we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic predictions. Extensive experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors.     

垂直学习社区基于学习兴趣与风格的社会化推荐算法

垂直学习社区包含了海量的学习资源,出现了信息过载现象,个性化推荐是解决这个难题的方法之一.但垂直学习社区中评分数据稀疏而文本、社交信息丰富,传统的协同过滤推荐算法不完全适用.基于用户产生的文本和行为信息,利用作者主题模型构建新的用户学习兴趣相似度衡量模型;根据用户交互行为信息综合考虑信任与不信任因素构建用户全面信任关系计算全面信任度;通过分析用户多维度学习行为模式,自动识别用户学习风格;最后提出融合兴趣相似度、全面信任度及学习风格的社会化推荐算法.用垂直学习社区网站CSDN实际数据集进行了实验分析.结果表明本文提出的推荐方法能更好向用户推荐其感兴趣的学习资源,有效地提高了推荐精度,进而提高用户学习效果.     

A novel social network hybrid recommender system based on hypergraph topologic structure

With the advent and popularity of social network, more and more people like to share their experience in social network. However, network information is growing exponentially which leads to information overload. Recommender system is an effective way to solve this problem. The current research on recommender systems is mainly focused on research models and algorithms in social networks, and the social networks structure of recommender systems has not been analyzed thoroughly and the so-called cold start problem has not been resolved effectively. We in this paper propose a novel hybrid recommender system called Hybrid Matrix Factorization(HMF) model which uses hypergraph topology to describe and analyze the interior relation of social network in the system. More factors including contextual information, user feature, item feature and similarity of users ratings are all taken into account based on matrix factorization method. Extensive experimental evaluation on publicly available datasets demonstrate that the proposed hybrid recommender system outperforms the existing recommender systems in tackling cold start problem and dealing with sparse rating datasets. Our system also enjoys improved recommendation accuracy compared with several major existing recommendation approaches.     

基于需求预测的主动服务推荐方法

在智能计算领域,网络中可用服务数量与类型的快速增长,使用户更依赖于服务完成各种业务,然而当前“请求-响应”被动式的服务模式严重影响了用户体验与资源利用率。为智能感知用户需求并主动为用户推荐合适的服务,通过引入需求预测过程,提出一种主动服务推荐方法。利用矩阵分解算法从大量历史服务使用数据中提取用户特征和服务特征,据此训练深度学习模型并预测用户的服务需求,进而为用户推荐其所需要的服务。基于真实数据的实验结果表明,该方法较单一的矩阵分解模型和深度神经网络模型具有更高的服务推荐准确性和稳定性。     

使用贝叶斯网络的社交网络信任驱动推荐方法

为处理推荐行为来源复杂、路径多样、不信任陌生推荐等问题,提出一种在社交网络中信任驱动推荐方法。该方法利用贝叶斯网络,计算用户评分的先验概率分布以及朋友之间的联合条件概率,预测用户在该环境下的评分并将推荐给用户。在信任驱动推荐过程中,预测评分既考虑到用户的偏好,也考虑到用户的社会关系;此外,用户的信息交换只限于朋友之间,能够有效保护用户的隐私。实验结果表明,所提出的推荐方法在预测准确率和推荐覆盖率上具有良好的性能。     

基于改进位置社交的近邻知识推荐算法

信息过载是当前各类网络中存在的普遍问题,社交网络中通过推荐算法为用户推荐感兴趣的内容,但该类算法并不适用于学习网络中存在特定逻辑联系的知识点推荐。结合社交网络及LBSN网络中的兴趣点推荐算法,提出了一种面向学习网络相关知识点的改进LBSN推荐算法,通过学习网络中的相似用户计算及知识路径发现,为用户推荐当前学习相关的近邻知识点,并通过实验数据证明了学习网络中加入学习推荐对学习者效率及学习质量提升的效果。     

Facebook single and cross domain data for recommendation systems

The emergence of social networks and the vast amount of data that they contain about their users make them a valuable source for personal information about users for recommender systems. In this paper we investigate the feasibility and effectiveness of utilizing existing available data from social networks for the recommendation process, specifically from Facebook. The data may replace or enrich explicit user ratings. We extract from Facebook content published by users on their personal pages about their favorite items and preferences in the domain of recommendation, and data about preferences related to other domains to allow cross-domain recommendation. We study several methods for integrating Facebook data with the recommendation process and compare the performance of these methods with that of traditional collaborative filtering that utilizes user ratings. In a field study that we conducted, recommendations obtained using Facebook data were tested and compared for 95 subjects and their crawled Facebook friends. Encouraging results show that when data is sparse or not available for a new user, recommendation results relying solely on Facebook data are at least equally as accurate as results obtained from user ratings. The experimental study also indicates that enriching sparse rating data by adding Facebook data can significantly improve results. Moreover, our findings highlight the benefits of utilizing cross domain Facebook data to achieve improvement in recommendation performance.     

基于降噪自动编码器的推荐算法

传统的推荐算法一般采用用户项目评分矩阵学习潜在因素,了解用户的个人偏好并作出推荐,但在实际应用中,评分矩阵通常非常稀疏。针对传统推荐算法的不足,提出一种基于降噪自动编码器的推荐模型。首先用2个自动编码器来训练用户和项目的潜在因子矩阵,然后将学习到的隐含特征向量输入一神经网络来进行评分预测,最后根据新的评分矩阵作出推荐。实验结果表明,该算法提高了推荐结果的召回率,同时缩小了重构误差。     

Social network-based service recommendation with trust enhancement

Given the increasing applications of service computing and cloud computing, a large number of Web services are deployed on the Internet, triggering the research of Web service recommendation. Despite of service QoS, the use of user feedback is becoming the current trend in service recommendation. Likewise in traditional recommender systems, sparsity, cold-start and trustworthiness are major issues challenging service recommendation in adopting similarity-based approaches. Meanwhile, with the prevalence of social networks, nowadays people become active in interacting with various computers and users, resulting in a huge volume of data available, such as service information, user-service ratings, interaction logs, and user relationships. Therefore, how to incorporate the trust relationship in social networks with user feedback for service recommendation motivates this work. In this paper, we propose a social network-based service recommendation method with trust enhancement known as RelevantTrustWalker. First, a matrix factorization method is utilized to assess the degree of trust between users in social network. Next, an extended random walk algorithm is proposed to obtain recommendation results. To evaluate the accuracy of the algorithm, experiments on a real-world dataset are conducted and experimental results indicate that the quality of the recommendation and the speed of the method are improved compared with existing algorithms.     

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.     

Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

Friend recommendation plays a key role in promoting user experience in online social networks (OSNs). However, existing studies usually neglect users’ fine-grained interest as well as the evolving feature of interest, which may cause unsuitable recommendation. In particular, some OSNs, such as the online learning community, even have little work on friend recommendation. To this end, we strive to improve friend recommendation with fine-grained evolving interest in this paper. We take the online learning community as an application scenario, which is a special type of OSNs for people to learn courses online. Learning partners can help improve learners’ learning effect and improve the attractiveness of platforms. We propose a learning partner recommendation framework based on the evolution of fine-grained learning interest (LPRF-E for short). We extract a sequence of learning interest tags that changes over time. Then, we explore the time feature to predict evolving learning interest. Next, we recommend learning partners by fine-grained interest similarity. We also refine the learning partner recommendation framework with users’ social influence (denoted as LPRF-F for differentiation). Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy (i.e., approximate 50% improvements on the precision and the recall) and can recommend learning partners with high quality (e.g., more experienced and helpful).     

Preference-based user rating correction process for interactive recommendation systems

In most of the recommendation systems, user rating is an important user activity that reflects their opinions. Once the users return their ratings about items the systems have suggested, the user ratings can be used to adjust the recommendation process.However, while rating the items users can make some mistakes (e.g., natural noises). As the recommendation systems receive more incorrect ratings, the performance of such systems may decrease. In this paper, we focus on an interactive recommendation system which can help users to correct their own ratings. Thereby, we propose a method to determine whether the ratings from users are consistent to their own preferences (represented as a set of dominant attribute values) or not and eventually to correct these ratings to improve recommendation. The proposed interactive recommendation system has been particularly applied to two user rating datasets (e.g., MovieLens and Netflix) and it has shown better recommendation performance (i.e., lower error ratings).     

DCFM：基于深度学习的混合推荐模型

传统推荐算法大多都仅考虑用户-商品评级信息来进行推荐,这种忽略了用户属性和商品属性信息的推荐模型准确率不高。因子分解机可在数据稀疏情况下挖掘用户与商品的关联关系,交叉网络可挖掘属性特征与其高阶特征的线性组合关系,以及深度神经网络有效识别高阶非线性关联关系,基于三种模型的优势,提出了一种基于深度学习的混合推荐模型（Deep and Cross Factorization Machine,DCFM）。三部分并联组合,共享输入层,各部分结果线性组合后作为模型整体输出。通过在MovieLens电影数据集上仿真实验,并与因子分解机（FM）、深度因子分解机（DeepFM）、深度交叉网络（DCN）模型做比较,结果证明该模型在准确率、F1-Score和AUC值上均得到了提高和改善。     

一种新的基于用户群体关系挖掘的随机漫游社会推荐模型

传统的协同过滤推荐算法在用户评分稀疏时,存在冷启动问题等不足,而最近几年提出的基于信任度的推荐算法以及一些它们的混合算法虽然解决了冷启动问题,却忽略了用户群体特征.针对上述情况,利用社会网络分析方法对社会性网络中的用户群体关系进行挖掘,提出一种全新的社会推荐模型Cliqueswalk,同时给出了权威推荐,为用户提供权威(意见领袖)的参考意见.实验表明,新的算法能够大大缩小目标评分信息的查找范围,推荐效率明显优于已有的协同过滤推荐算法、基于信任度的推荐算法以及它们的混合算法.