基于标签改进社交网络好友推荐算法研究与应用

随着信息技术的快速发展,人们的社交网络方式也在不断地改变,因此如何准确地给用户个性化的推荐成为当前研究的热点。文中通过对传统的基于标签的推荐算法进行了深入的研究,针对其推荐准确率低和缺乏新颖性的问题,提出了一种改进的基于标签的推荐算法模型。通过仿真实验并与腾讯微博推荐算法进行对比测试,得出改进的算法不仅能够推荐与目标用户相似的用户,还能挖掘具有相同兴趣爱好的潜在用户,最终验证了改进的标签算法能够有效地提高推荐系统准确率和新颖性。     

一种社交网络中朋友推荐算法的研究

针对云计算环境下的社交网络中朋友推荐中可能存在大量冗余,无效信息等缺点,提出了基于猴群算法的朋友社区推荐方案,该方案利用爬虫程序获得的新浪微博好友数据集,对用户所在的社区进行划分,并进一步使用猴群算法对社区中的朋友链接关系进行了划分.仿真实验中将该算法与基于用户综合相似度的推荐算法在查准率,查全率和F1指标方面进行对比...     

社交媒体平台算法推荐过程中的伦理问题探究

在人工智能大数据时代，推荐算法已成为操控人们信息获取、价值判断的一双无形之手。社交网络平台发展至今，离不开推荐算法技术的成熟和普及。算法通过分析用户数据、描绘用户画像实现“千人千面”的精准分发，然而也不可避免地遇到一系列伦理问题。本文着眼于推荐算法在面对接受者、公众、消费者三个维度的用户时所产生的伦理问题，并提出通过用户、平台、法律三方面的共同努力对推荐算法的伦理问题进行治理。     

融合地理影响的个性化位置推荐

位置推荐是近年来的研究热点,矩阵分解由于其在推荐算法中的良好表现而得到普遍应用。但是,传统的矩阵分解算法由于是直接对评分矩阵进行分解,没有考虑其他影响因素,因而容易造成推荐准确性偏低。本文研究了地理位置对于签到行为的影响,提出了结合地理因素的协同过滤推荐方法。实验结果表明,本文提出的方法能够有效地预测用户评分,提升推荐精度。     

融入音乐子人格特质和社交网络行为分析的音乐推荐方法

为了可以实时推荐符合人们情感状态的音乐,提出了一种融入音乐子人格特质的社交网络行为分析的音乐推荐算法,该算法通过分析用户发表在微博等社交媒体上的状态,计算用户在该情感状态下对音乐的偏好程度;选择在该情感状态下音乐偏好相似的最近邻用户,最后融入音乐子人格特质进行偏好度计算,为用户推荐最适合其情感状态的音乐。实验结果表明,该算法可以缓解用户数据稀疏性对推荐结果的影响,能够提高推荐系统的推荐质量。     

基于网络结构改进社交网络好友推荐算法研究

随着信息技术的快速发展,人们的社交网络方式不断地改变,如何准确地给用户个性化推荐成为当前研究的热点。文中通过对基于网络结构的推荐技术深入的研究,针对该技术中随机游走模型存在的计算开销大、偏离目标及未考虑顶点重要性等不足,建立了一种基于连通性的局部随机游走重启动模型(Connectivity Local Random Walk With Restart Recommendation Algorithm,简称C-LRWR)。通过仿真实验与图模型中常见的推荐算法进行对比及测试,最后得出改进的C-LRWR算法能够提高推荐的准确率。     

一种社交网络群组间信息推荐的有效方法

群组间信息推荐是社交网络中人们传递和分享资讯的重要途径,然而获取精确的最优推荐方案需要指数级时间开销.为此,本文提出一种有效算法EAOORS(Efficient Algorithm for Obtaining Optimal Recommendation Solution),将该指数级时间开销问题等价归约为EST(Extended Steiner Tree,扩展Steiner树)问题,并在多项式时间复杂度内快速获取近似最优推荐方案.理论分析和仿真实验表明,本文所提的算法具有有效性和实用性.     

基于深度学习与社交感知的地点推荐

随着基于位置的社交网络的普及,地点推荐作为推荐系统的重要分支,在解决信息过载、提升用户体验、增加平台收益等方面的作用愈加明显。现有的地点推荐算法大多基于矩阵分解,难以刻画用户和地点之间复杂的交互关系;此外,在基于位置的社交网络中,社交信息是建立用户画像的重要数据来源,如何融合社交信息辅助地点推荐成为亟待解决的问题。本文研究了基于深度神经网络的地点推荐解决方案,通过设计基于社交信息的新型采样方式和正则化损失函数,从两个角度融合社交信息。在两个真实世界数据集上的实验表明,本文提出的方案可以极大提升地点推荐的精准度。     

异质社交网络中多通道特征融合的好友推荐模型

合理有效的好友推荐算法对于社交网络的发展和扩张有重大的意义。然而随着社交网络的复杂化和异质化,传统推荐系统中协同过滤推荐方法不能满足需求。针对异质社交网络中存在着大量的内容相关信息这一特点,根据好友推荐的需求,提出了多通道特征融合的好友推荐模型。该模型对用户相关的多维特征进行挖掘与利用,包括显性特征(如用户profile,用户tag,社交关系等)和隐性特征(如用户重要度,挖掘用户标注发现其领域兴趣等),并进一步将这些内容相关的多特征融合到协同排序算法中进行学习训练。实验结果表明,随着多个内容特征的逐步融合,算法的MAP值稳步提高,最终相对未融合的协同排序方法提高了12%,并在一定程度上的解决了冷启动问题,提高了好友推荐的多样性。     

位置社交网络中谱嵌入增强的兴趣点推荐算法

为了有效地捕捉LBSN中丰富的签到、社交等多维上下文信息的空间特性,并深层挖掘用户和POI之间的非线性交互,提出了一种谱嵌入增强的POI推荐算法——PSC-SMLP,设计了偏好增强的谱聚类算法PSC和谱嵌入增强的神经网络SMLP。在2个经典数据集上与现有的POI推荐算法相比,PSC-SMLP可以深层学习用户对POI的个性化偏好,在准确率、召回率、n DCG、平均精度等指标中均获得较大提升。     

融合多权重因素的低秩概率矩阵分解推荐模型

针对个性化推荐精度较低、对冷启动敏感等问题,该文提出一种融合多权重因素的低秩概率矩阵分解推荐模型MWFPMF.模型利用给定的社交网络构建信任网络,借助Page rank算法和信任传递机制求取用户间信任度;基于Page rank计算用户社会地位,利用活动评分和评分时间修正用户间关系权重;引入词频-逆文本频率技术(TF-I...     

基于k-距离与重加权M-估计量的鲁棒协同推荐算法(英文)

The existing collaborative recommendation algorithms have lower robustness against shilling attacks.With this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator.Firstly,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor model.The influence of attack profiles on the recommendation results is reduced through adjusting similarities among users.Then,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature matrix.Finally,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization model.Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.     

Joint recommendation algorithm based on tensor completion and user preference

Aiming at the problem that existing recommendation algorithms have little regard for user preference,and the recommendation result is not satisfactory,a joint recommendation algorithm based on tensor completion and user preference was proposed.First,a user-item-category 3-dimensional tensor was built based on user-item scoring matrix and item-category matrix.Then,the Frank-Wolfe algorithm was used for iterative calculation to fill in the missing data of the tensor.At the same time,a user category preference matrix and a scoring preference matrix were built based on the 3-dimensional tensor.Finally,a joint recommendation algorithm was designed based on the completed tensor and the two preference matrices,and the differential evolution algorithm was used for parameter tuning.The experimental results show that compared with some typical and newly proposed recommendation algorithms,the proposed algorithm is superior to the compare algorithms,the precision is improved by 1.96% ~ 3.44% on average,and the recall rate is improved by 1.35%~2.40% on average.     

基于类别转移加权张量分解模型的兴趣点分区推荐

基于位置社交网络的兴趣点(POI)推荐是人们发现有趣位置的重要途径,然而,现实中用户在不同区域的地点偏好侧重的差异,加之高维度的历史签到信息,使得精准而又个性化的POI推荐极富挑战性.对此,该文提出一种新型的基于类别转移加权张量分解模型的兴趣点分区推荐算法(WTD-PR).通过结合用户连续行为和时间特征,来充分利用用户...     

基于用户相似度的协同过滤推荐算法简

协同过滤推荐算法通过研究用户的喜好,实现从海量数据资源中为用户推荐其感兴趣的内容,在电子商务中得到了广泛的应用。然而,当此类算法应用到社交网络时,传统的评价指标与相似度计算的重点发生了变化,从而出现推荐算法效率偏低,推荐准确度下降问题,导致社交网络中用户交友推荐满意度偏低。针对这一问题,引入用户相似度概念,定义社交网络中属性相似度,相似度构成与计算方法,提出一种改进的协同过滤推荐算法,并给出推荐质量与用户满意度评价方法。实验结果表明：改进算法能有效改善社交网络中的推荐准确性并提高推荐效率,全面提高用户满意度。     

基于受限信任关系和概率分解矩阵的推荐

现有的推荐算法很难对没有任何记录的冷启动用户或者历史记录稀疏的用户给出准确的推荐,即用户的冷启动问题.本文提出一种基于受限信任关系和概率分解矩阵的推荐方法,由不信任关系约束信任关系的传播,得到准确且覆盖全面的用户信任关系矩阵,并通过对用户信任关系矩阵和用户商品矩阵的概率分解联合用户信任关系和用户商品矩阵信息,为用户给出推荐.实验表明该方法对冷启动用户和历史记录稀疏的用户的推荐效果有较大幅度的提升,有效地解决了用户的冷启动问题.     

综合项目评分和属性的个性化推荐算法

针对传统协同过滤算法存在的数据稀疏性和冷启动问题,提出了一种综合项目评分和属性的个性化推荐算法.该算法在衡量项目相似性时,同时考虑用户评分和项目属性特征,并根据评分数据的实际稀疏情况动态调整两者的影响权重;预测评分时,利用用户对项目属性的偏好度来衡量其对未评分邻居项的喜好程度,并产生最终推荐.基于MovieLens数据集进行的实验结果表明,该算法使得最近邻的确定更加准确,系统推荐质量明显改善.     

POI recommendation by incorporating trust-distrust relationship in LBSN

POI (point of interest) recommendation is an important personalized service in the LBSN (location-based social network) which has wide applications such as popular sights recommendation and travel routes planning.Most existing collaborative filter algorithms make recommendation according to user similarity and location similarity,they don’t consider the trust relationship between users.And trust relationship is helpful to improve recommendation accuracy,robustness and user satisfaction.Firstly,the propagation property of trust and distrust relationship was analyzed.Then,the measurement and computation method of trust were given.Finally,a hybrid recommendation system which combined user similarity,geographical location similarity and trust relationship was proposed.The experiments results show that the hybrid recommendation is obviously superior to the traditional collaborative filtering in terms of results accuracy and user satisfaction.     

Co-pairwise ranking model for item recommendation

Most of existing recommendation models constructed pairwise samples only from a user’s perspective.Nevertheless,they overlooked the functional relationships among items--A key factor that could significantly influence user purchase decision-making process.To this end,a co-pairwise ranking model was proposed,which modeled a user’s preference for a given item as the combination of user-item interactions and item-item complementarity relationships.Considering that the rank position of positive sample and the negative sampler had a direct impact on the rate of convergence,a rank-aware learning algorithm was devised for optimizing the proposed model.Extensive experiments on four real-word datasets are conducted to evaluate of the proposed model.The experimental results demonstrate that the devised algorithm significantly outperforms a series of state-of-the-art recommendation algorithms in terms of multiple evaluation metrics.     

Social recommendation algorithm fusing user interest social network

Using the social information among users in recommender system can partly solve the data sparsely problems and significantly improve the performance of the recommendation system. However, the recommendation systems which using the users' social information have two main problems: the explicit user social connection information is not always available in real-world recommender systems, and the user social connection information is directly used in recommender systems when the user explicit social information is available. But as we know that the user social information is not all based on user interest, so this can introduce noise to the recommender systems. This paper proposes a social recommender system model called interest social recommendation (ISoRec). Based on probability matrix factorization (PMF), the model addresses the problems mentioned above by combining user-item rating matrix, explicit user social connection information and implicit user interest social connection information to make more accurately recommendation. In addition, the computational complexity of our algorithm is linear with respect to the number of observed data sets used in this algorithm, and can scalable to very large datasets.