学术社交网络中的权威学者推荐模型

目前，学术社交网络平台存在的信息过载和信息不对称等问题导致学者特别是影响力低的学者很难找到自己感兴趣的内容，同时，学术社交网络中影响力大的学者对学术社区的形成具有一定的促进作用并且对影响力低的学者的科学研究具有一定的导向作用，因此提出一种融合学术社区检测的权威学者推荐模型（ISRMACD）来为学术社交网络中的低影响力学者提供推荐服务。首先，利用影响力大的学者圈作为社区的核心结构对学术社交网络中学者间的关系纽带——好友关系所产生的复杂网络拓扑关系进行学术社区检测；然后，对社区内的学者计算影响力，并实现社区内部的权威学者推荐服务。在学者网数据集上的实验结果表明，该推荐模型在不同的权威学者推荐数量下均取得了较高的推荐质量，并且每次推荐10名权威学者取得的推荐精度最高，达到70%及以上。     

学术社交网络中的权威学者推荐模型

目前，学术社交网络平台存在的信息过载和信息不对称等问题导致学者特别是影响力低的学者很难找到自己感兴趣的内容，同时，学术社交网络中影响力大的学者对学术社区的形成具有一定的促进作用并且对影响力低的学者的科学研究具有一定的导向作用，因此提出一种融合学术社区检测的权威学者推荐模型（ISRMACD）来为学术社交网络中的低影响力学者提供推荐服务。首先，利用影响力大的学者圈作为社区的核心结构对学术社交网络中学者间的关系纽带——好友关系所产生的复杂网络拓扑关系进行学术社区检测；然后，对社区内的学者计算影响力，并实现社区内部的权威学者推荐服务。在学者网数据集上的实验结果表明，该推荐模型在不同的权威学者推荐数量下均取得了较高的推荐质量，并且每次推荐10名权威学者取得的推荐精度最高，达到70%及以上。     

Reciprocal Recommender Systems: Analysis of state-of-art literature,challenges and opportunities towards social recommendation

There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a large city. Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on the user’s preferences, needs and/or behavior. Unlike most conventional recommender approaches where items are inanimate entities recommended to the users and success is solely determined upon the end user’s reaction to the recommendation(s) received, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users. Hence, both the end user and the user being recommended should accept the “matching” recommendation to yield a successful RRS performance. The operation of an RRS entails not only predicting accurate preference estimates upon user interaction data as classical recommenders do, but also calculating mutual compatibility between (pairs of) users, typically by applying fusion processes on unilateral user-to-user preference information. This paper presents a snapshot-style analysis of the extant literature that summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS, both inherited from conventional user-to-item recommendation models and those inherent to this emerging family of approaches. Representative RRS models are likewise highlighted. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.     

Trust based consensus model for social network in an incomplete linguistic information context

A theoretical framework to consensus building within a networked social group is put forward. This article investigates a trust based estimation and aggregation methods as part of a visual consensus model for multiple criteria group decision making with incomplete linguistic information. A novel trust propagation method is proposed to derive trust relationship from an incomplete connected trust network and the trust score induced order weighted averaging operator is presented to aggregate the orthopairs of trust/distrust values obtained from different trust paths. Then, the concept of relative trust score is defined, whose use is twofold: (1) to estimate the unknown preference values and (2) as a reliable source to determine experts’ weights. A visual feedback process is developed to provide experts with graphical representations of their consensus status within the group as well as to identify the alternatives and preference values that should be reconsidered for changing in the subsequent consensus round. The feedback process also includes a recommendation mechanism to provide advice to those experts that are identified as contributing less to consensus on how to change their identified preference values. It is proved that the implementation of the visual feedback mechanism guarantees the convergence of the consensus reaching process.     

Contextualized mobile recommendation service based on interactive social network discovered from mobile users

Personal context is the most significant information for providing contextualized mobile recommendation services at a certain time and place. However, it is very difficult for service providers to be aware of the personal contexts, because each person’s activities and preferences are very ambiguous and depending on numerous unknown factors. In order to deal with this problem, we have focused on discovering social relationships (e.g., family, friends, colleagues and so on) between people. We have assumed that the personal context of a certain person is interrelated with those of other people, and investigated how to employ his neighbor’s contexts, which possibly have a meaningful influence on his personal context. It indicates that we have to discover implicit social networks which express the contextual dependencies between people. Thereby, in this paper, we propose an interactive approach to build meaningful social networks by interacting with human experts. Given a certain social relation (e.g., isFatherOf), this proposed systems can evaluate a set of conditions (which are represented as propositional axioms) asserted from the human experts, and show them a social network resulted from data mining tools. More importantly, social network ontology has been exploited to consistently guide them by proving whether the conditions are logically verified, and to refine the discovered social networks. We expect these social network is applicable to generate context-based recommendation services. In this research project, we have applied the proposed system to discover the social networks between mobile users by collecting a dataset from about two millions of users.     

User comments for news recommendation in forum-based social media

News recommendation and user interaction are important features in many Web-based news services. The former helps users identify the most relevant news for further information. The latter enables collaborated information sharing among users with their comments following news postings. This research is intended to marry these two features together for an adaptive recommender system that utilizes reader comments to refine the recommendation of news in accordance with the evolving topic. This then turns the traditional “push-data” type of news recommendation to “discussion” moderator that can intelligently assist online forums. In addition, to alleviate the problem of recommending essentially identical articles, the relationship (duplicate, generalization, or specialization) between recommended news articles and the original posting is investigated. Our experiments indicate that our proposed solutions provide an improved news recommendation service in forum-based social media.     

A significance-based trust-aware recommendation approach

Trust-aware recommender systems have been widely used in recent years to improve the performance of traditional collaborative filtering systems. A common assumption of existing trust models is that all items have the same importance for all users. However, it is reasonable to expect that some items are more significant than others in making recommendations. Furthermore, the significance of an item is not the same for all users but varies depending on many factors such as the demographic characteristics of users. For example, an item that is important to women may not be important to men. Also, the significance of an item for an individual user is not static and can change throughout the life cycle (from childhood to old age). Thus, items that are currently important to a user may become less important in the future. In this paper, we propose a Significance-Based Trust-Aware Recommendation (SBTAR) approach, which uses a new trust measure based on the concept of item significance. The significance of an item for a user is measured with respect to the demographic context of the user. Thus, SBTAR can adapt to dynamic changes in user preferences. To model demographic context, SBTAR uses Shuffled Frog Leaping Algorithm (SFLA), which is a meta-heuristic optimization technique based on the social behavior of frogs. SFLA has the advantages of simplicity, fast convergence, strong global search ability and easy implementation. Experimental results show that the proposed approach is more effective and efficient than several state-of-the-art recommendation approaches.     

A social recommendation approach for reward-based crowdfunding campaigns

In recent years, a new kind of fundraising mode known as crowdfunding has gradually emerged. Because of the rapid spread of the internet, people can offer their creative ideas on a fundraising platform and attract mass backers to invest in their projects. Crowdfunding not only helps users to realize their dreams but also allows companies to carry out test marketing. Although crowdfunding brings huge opportunities, the success rate of fundraising plans remains low. In this study, we therefore propose a phase-based backer recommendation mechanism, which integrates information from crowdfunding and social networking platforms by analyzing the factors of social relationships, user preferences, and economic backgrounds, to help project creators to reach their fundraising goals at each stage. Our experimental results show that the proposed mechanism is effective in identifying appropriate backers with respect to the status of the project and significantly improves the success rates of crowdfunding. The proposed mechanism can provide greater business value and more opportunities to crowdfunding platforms and contribute to more successful fundraising plans.     

Trust-aware recommendation for improving aggregate diversity

Recommender systems are becoming increasingly important and prevalent because of the ability of solving information overload. In recent years, researchers are paying increasing attention to aggregate diversity as a key metric beyond accuracy, because improving aggregate recommendation diversity may increase long tails and sales diversity. Trust is often used to improve recommendation accuracy. However, how to utilize trust to improve aggregate recommendation diversity is unexplored. In this paper, we focus on solving this problem and propose a novel trust-aware recommendation method by incorporating time factor into similarity computation. The rationale underlying the proposed method is that, trustees with later creation time of trust relation can bring more diverse items to recommend to their trustors than other trustees with earlier creation time of trust relation. Through relevant experiments on publicly available dataset, we demonstrate that the proposed method outperforms the baseline method in terms of aggregate diversity while maintaining almost the same recall.     

GeoSRS: A hybrid social recommender system for geolocated data

We present GeoSRS, a hybrid recommender system for a popular location-based social network (LBSN), in which users are able to write short reviews on the places of interest they visit. Using state-of-the-art text mining techniques, our system recommends locations to users using as source the whole set of text reviews in addition to their geographical location. To evaluate our system, we have collected our own data sets by crawling the social network Foursquare. To do this efficiently, we propose the use of a parallel version of the Quadtree technique, which may be applicable to crawling/exploring other spatially distributed sources. Finally, we study the performance of GeoSRS on our collected data set and conclude that by combining sentiment analysis and text modeling, GeoSRS generates more accurate recommendations. The performance of the system improves as more reviews are available, which further motivates the use of large-scale crawling techniques such as the Quadtree.     

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。     

基于位置的社会化网络的并行化推荐算法

针对传统的协同过滤算法在利用签到记录进行兴趣点(POI)推荐时不能充分利用签到信息所隐含的偏好、位置和社交网络信息而损失准确率的问题,以及传统的单机串行算法在大数据处理能力上的弱势,提出一种基于位置和朋友关系的协同过滤(LFBCF)算法,以用户历史偏好为基础,综合考虑用户社交关系网络进行协同过滤,并以用户的活动范围作为约束实现对用户的兴趣点推荐。为了支持大数据量的实验,将算法在Spark分布式计算平台上进行了并行化实现。研究过程中使用了Gowalla和Brightkite这两个基于位置的社会化网络数据集,分析了数据集中签到数量、签到位置之间距离、社交关系等可能对推荐结果造成影响的因素,以此来支持提出的算法。实验部分通过与传统的协同过滤算法等经典算法在准确率、F-measure上的对比验证了算法在推荐效果上的优越性,并通过并行算法与单机串行算法在不同数据规模上加速比的对比验证了算法并行化的意义以及性能上的优越性。     

社会网络环境下基于信任传递的推荐模型研究

现有基于信任的推荐算法中没有充分挖掘用户间的信任关系,且缺乏合理的信任关系传递规则,极大地影响了推荐算法的可靠性和准确性。针对上述问题,通过用户评分数据与用户的社会关系建立信任传递模型,提出一种基于信任传递的推荐算法。该算法首先利用评分数据计算信任传递模型中用户的隐式直接信任关系,其次通过求解有序加权平均算子融合多条信任传递链的间接信任关系,最后将计算出的用户信任度与相似度融合为综合相似度进行预测推荐。实验结果证实了所提算法可有效提升系统的推荐质量。     

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.     

基于社交网络的一种个性化推荐算法

随着近些年社交网络的流行,如何有效地利用社交网络资源是推荐算法的热点问题,大多数推荐系统都是以评分等手段获取新的数据再通过计算给出用户推荐序列,但是如果能有效地利用社交网络资源,就可以减少评分这一步骤,对用户来说也更加便利。本文借鉴了部分社会心理学原理,提出了人与人之间由相似产生的信任度计算方法,对已有的由熟悉性产生的信任度计算方法给予改进,并与改进前作出对比,验证了其现实意义以及有效性。     

基于社会网络分析的协同推荐方法改进

协同推荐是电子商务中被广泛使用的个性化服务技术,但由于数据稀疏、冷启动等原因,导致现有协同推荐方法的个性化服务水平不高。为提高协同推荐的准确性,利用社会网络分析对协同推荐方法加以改进,提出一种基于社会网络分析改进的协同推荐方法。该方法利用社会网络分析技术分析用户间的关系,将其量化为信任度以填充用户-项矩阵,并将信任度融入到用户相似性计算中。通过实验分析验证了所提方法的有效性。以信任度扩充用户-项矩阵不仅可以较好地解决协同推荐中数据稀疏和冷启动问题,而且能够提高协同推荐的准确性。     

基于胶囊网络的交互式网络电视视频点播推荐模型

在交互式网络电视（IPTV）应用中,家庭电视终端往往由多名家庭成员共用,现有推荐算法难以从终端历史数据中分析出家庭成员的不同兴趣偏好。为了满足同一终端下不同成员的视频点播需求,提出了一种基于胶囊网络的IPTV视频点播推荐模型CapIPTV。首先,设计了一种基于胶囊网络路由机制的用户兴趣生成层,将终端历史行为数据作为输入,并通过胶囊网络的聚类特性得到不同家庭成员的兴趣表达;其次,利用注意力机制给不同的兴趣表达动态分配注意力权重;最后,提取出不同家庭成员的兴趣向量和点播视频的表示向量,计算两者内积后得出Top-N偏好推荐。在公开数据集MovieLens和真实广电数据集IPTV上的实验结果表明,CapIPTV的命中率（HR）、召回率（Recall）和归一化折损累计增益（DNCG）优于其他五种同类推荐模型。     

基于用户话题偏好的社会网络二级人脉推荐

社会网络（SNS）用户的社交圈和人脉关系研究多采用图论的知识,对社会网络关系图的节点和边进行探讨,没有考虑到用户自身的兴趣偏好,因此提出了一种基于用户话题偏好的二级人脉推荐方法。利用文本挖掘的相关技术和最小均方误差（LMS）算法,把抓取到的用户话题数据合理地转化为用户话题偏好特征向量,用相似度度量方法来计算用户之间的相似度,以确定与用户话题偏好最相近的用户集,并完成用户的二级好友推荐。实验表明,推荐的二级好友采纳率达到70%。     

基于社会网络与声誉信任机制的移动多Agent系统信任模型

基于信任机制的移动多Agent系统中,代理Agent一般通过直接信誉值和推荐信誉值来判断对于另一个Agent的信任程度。由于系统相对巨大,直接信誉值通常难以获得,判断的正确性很大程度上依赖于推荐信誉值的准确性和可靠性。通过对整个多Agent系统进行社会网络的挖掘,用以得到与代理Agent存在潜在社会关系的一组Agent。对这组Agent提供的推荐信息充分信任,并优先使用这些Agent提供的信息进行推荐信誉值的计算。最后通过双方直接交易的多寡判断综合信任值中直接信誉值与推荐信誉值的权重。通过实验验证了该模型的有效性。