兴趣点推荐方法研究综述

兴趣点(Point-Of-Interest,POI)推荐是基于位置的社交网络(Location-Based Social Networks,LBSN)中一项重要的服务,无论对商家还是对客户都有重要的影响,并且兴趣点数据作为时空数据的典型更是得到了广泛关注,因此兴趣点推荐近年来已经成为学术界的热门研究课题.文章分析了兴趣点推荐的影响因素,对传统兴趣点推荐方法进行了总结,分析了最新的基于图嵌入方法以及图神经网络在兴趣点推荐领域中的应用,最后对兴趣点推荐所面临的挑战以及未来的研究趋势加以分析.     

Recommendation system for localized products in vending machines

This paper proposes a framework of localized product recommendation system for automatic vending machines applications. The goal is to offer suitable recommendations of localized products to customers in distinct locations. We develop a hybrid technique that combines a meta-heuristic approach, clustering technique, classification, and statistical method. In the approach, an intelligent system is implemented to analyze product attributes and determine localized products based on the transaction data. To prove the feasibility and effectiveness of proposed approach, we implemented the system in several automatic vending machines owned by an information service company of Taiwan. Nine machines were selected and compared from two locations: living lab by Institute for Information Industry of Taiwan at Song-shan District and business office building at Nei-hu District in Taipei. The real life experiments showed that the profit of vending machine increases after applying our system.     

PTRA:一个面向空载出租车的路线推荐算法

目前针对出租车的推荐系统主要从降低空载率和减少寻客里程两个方面为司机推荐潜在载客点或最优行驶路线,然而从司机收益最大化的角度而言,多数研究没有考虑实时路况的变化对推荐效果的影响.因此,以收益最大化为目标进行了研究,提出了一种空载出租车推荐算法PTRA(profit-based taxi recommendation a...     

基于隐性反馈与交叉推荐的癫痫药物推荐系统

癫痫是一种较为常见的脑部疾病,也是一种长期性神经系统疾患.目前全球有大量的患者饱受癫痫带来的痛苦.癫痫虽然不能根治,但是约70％癫痫病例的抽搐发作可以通过药物控制.电子健康病历蕴含着众多癫痫患者的信息,为个性化药物的处方提供了海量大数据资源.文章通过对医疗电子病例进行大数据分析,提出一种基于隐性反馈模型与交叉推荐的药物推荐方法(Implicit Feedback and CrossingRecommendation,IFCR),以帮助医生选择合适的药物.该方法通过分析患者的看病历史以及相似患者的看病经历,建立患者症状与医生用药之间的对应关系,从而根据患者症状为医生提供药物推荐.与基于人工神经网络的药物推荐系统进行对比试验发现,文章提出的药物推荐系统在召回率上具有显著优势,而在精确率上二者各有优劣.总体来看,文章所提出的IFCR方法效果更为出色.最后,通过对两种方法的推荐结果做进一步分析发现,二者推荐倾向不同,因此存在建立集成模型的可能.     

GeoPMF:距离敏感的旅游推荐模型

虽然目前旅游者可以利用Web搜索引擎来选择旅游景点,但往往难以获得较好符合自身需要的旅游规划.而旅游推荐系统是解决上述问题的有效方式.一个好的旅游推荐模型应具有个性化并能考虑用户时间和费用的限制.调研表明,用户在选择旅游景点时,目的地与用户常居地的距离常常是一个需要考虑的问题.因为旅行距离往往可以间接地反映了时间和费用的影响.于是,在贝叶斯模型和概率矩阵分解模型的基础上,提出一个旅行距离敏感的旅游推荐模型(geographical probabilistic matrix factorization, GeoPMF).主要思想是基于每个用户的旅游历史,推算出一个最偏好的旅游距离,并作为一种权重,添加到传统的基于概率矩阵分解的推荐模型中.在携程网站的旅游数据集上的实验表明,与基准方法相比,GeoPMF 的RMSE(root mean square error)可以降低近10%;与传统概率矩阵分解模型(PMF)相比,通过考虑距离因子,RMSE平均降幅近3.5%.     

一种面向微博用户的标签推荐方法

首先对国内微博平台的信息进行了综合分析,主要介绍了微博信息的定义,在错综复杂的微博信息中哪些信息比较重要,以及这些微博信息包含哪些详细的内容,是如何组织的。然后选取新浪微博平台作为研究对象,利用新浪微博API设计了爬虫程序,抽取用户信息;以用户的关注人数、粉丝数和发布的微博数为标准对用户信息进行了定量分析。最后根据分析结果,针对不同特征的用户群体提出了相应的标签推荐方法。     

一种面向微博用户的标签推荐方法

首先对国内微博平台的信息进行了综合分析,主要介绍了微博信息的定义,在错综复杂的微博信息中哪些信息比较重要,以及这些微博信息包含哪些详细的内容,是如何组织的。然后选取新浪微博平台作为研究对象,利用新浪微博API设计了爬虫程序,抽取用户信息;以用户的关注人数、粉丝数和发布的微博数为标准对用户信息进行了定量分析。最后根据分析结果,针对不同特征的用户群体提出了相应的标签推荐方法。     

一种面向共享账号的个性化推荐算法

传统的推荐领域的研究主要面向的是单个用户的内容推荐,然而现实生活中多个用户共享同一个账号的情况不在少数。为了解决多用户共享账号情况下,账号内部分用户得不到有效推荐的问题,本文提出PRASA(Personalized Recommendation Algorithm for Shared Account)算法,首先利用LDA(Latent Dirichlet Allocation)主题模型构建项目特征向量,接着利用DPC(Density Peaks based Clustering)算法对项目进行聚类分组,为分组后的每组项目分别进行推荐,对于离群点进行单独处理后产生推荐,保证推荐结果可以覆盖更广泛。实验结果表明,本文提出的PRASA算法可以有效地为共享账号的用户产生合适的推荐。     

一种支持个性化Web推荐的聚类分析

提出基于关联的聚类分析方法,挖掘具有相似访问兴趣的用户访问模式,分离不相关的用户模式,并提出基于关联的聚类算法。实验证明,该算法大量减少不相关的用户访问模式,提高个性化推荐质量。为进一步研究个性化推荐技术奠定基础。     

基于Web使用挖掘的个性化学习推荐系统

针对当前E learning推荐系统存在的问题，引入多Agent（MAS）系统，提出一种基于Web使用挖掘的集成MAS与Web services的分布式智能推荐系统模型。该模型能动态生成基于用户使用信息的个性化链接页面，有效地帮助学员找到所需的资源信息。提出了一种基于最近最少使用策略的系统推荐算法，包括系统整体实现算法、系统聚类算法及推荐算法。实时性能分析显示该系统运行性能良好。     

Recommendation system based on product purchase analysis

With the advent of online marketplaces, the buying practices have changed. The Amazon co-purchase network provides us with dynamic snapshots of co-purchases. We analyse the various properties of the graph like clustering co-efficient, degree distributions, etc. and try to reason out the underlying relation between such distributions. In an attempt to understand the motifs in buying pattern, we propose algorithms to mine patterns of interest. In the end, we take into account all these various parameters and develop recommendation algorithms to suit the needs of the customers.     

Trust-Based Personalized Service Recommendation: A Network Perspective简

Recent years have witnessed a growing trend of Web services on the Interact. There is a great need of effective service recommendation mechanisms. Existing methods mainly focus on the properties of individual Web services （e.g., func- tional and non-functional properties） but largely ignore users＇ views on services, thus failing to provide personalized service recommendations. In this paper, we study the trust relationships between users and Web services using network modeling and analysis techniques. Based on the findings and the service network model we build, we then propose a collaborative filtering algorithm called Trust-Based Service Recommendation （TSR） to provide personalized service recommendations. This systematic approach for service network modeling and analysis can also be used for other service recommendation studies.     

云计算环境下基于协同过滤的个性化推荐机制

随着云计算时代的到来,应用数据量剧增,个性化推荐技术日趋重要.然而由于云计算的超大规模以及分布式处理架构等特点,将传统的推荐技术直接应用到云计算环境时会面临推荐精度低、推荐时延长以及网络开销大等问题,导致推荐性能急剧下降.针对上述问题,提出一种云计算环境下基于协同过滤的个性化推荐机制RAC.该机制首先制定分布式评分管理策略,通过定义候选邻居(candidate neighbor, CN)的概念筛选对推荐结果影响较大的项目集,并构建基于分布式存储系统的2个阶段评分索引,保证推荐机制快速准确地定位候选邻居;在此基础上提出基于候选邻居的协同过滤推荐算法(candidate neighbor-based distribited collaborative filtering algorithm, CN-DCFA),在候选邻居中搜索目标用户已评分项目的k近邻,预测目标用户的推荐集top-N.实验结果表明,在云计算环境下RAC拥有良好的推荐精度和推荐效率.     

A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce

Collaborative filtering is one of the most widely adopted and successful recommendation approaches. Unlike approaches based on intrinsic consumer and product characteristics, CF characterizes consumers and products implicitly by their previous interactions. The simplest example is to recommend the most popular products to all consumers. Researchers are advancing CF technologies in such areas as algorithm design, human- computer interaction design, consumer incentive analysis, and privacy protection.     

一种基于内容过滤的科技文献推荐算法

个性化推荐技术能够帮助用户快速方便地从大量的电子文献中获得感兴趣的文献,但传统的基于内容过滤的推荐算法不能反映用户对文献需求的兴趣变化,难以区分文献质量的高低。针对上述问题,提出了基于用户访问时间的数据权重和文献重要度,以便更好反应用户的兴趣以及文献质量的优劣,并将这两种度量引入到基于内容过滤的科技文献推荐算法中。实验和分析表明,改进后的算法比传统的内容过滤推荐算法在对文献推荐的准确度上有所提高。     

A Generative Model Approach for Geo-Social Group Recommendation

With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem: 1) the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2) users have different influences when in different groups. In this paper, we propose a generative geo-social group recommendation model (GSGR) to recommend points of interest (POIs) for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.     

一种基于内容过滤的科技文献推荐算法

个性化推荐技术能够帮助用户快速方便地从大量的电子文献中获得感兴趣的文献,但传统的基于内容过滤的推荐算法不能反映用户对文献需求的兴趣变化,难以区分文献质量的高低.针对上述问题,提出了基于用户访问时间的数据权重和文献重要度,以便更好反应用户的兴趣以及文献质量的优劣,并将这两种度量引入到基于内容过滤的科技文献推荐算法中.实验和分析表明,改进后的算法比传统的内容过滤推荐算法在对文献推荐的准确度上有所提高.     

一种面向科研在线的个性化推荐系统

电子邮箱的推送功能对信息传播和推广有重要作用,而科研在线云平台的邮箱推送功能出现信息过载问题后却使得推送邮件可能变成垃圾邮件。为解决该问题,本文介绍了一种基于科研在线云平台的个性化推荐系统,通过推荐模型将资源进行排序,并按周选取得分最高的一部分资源通过推送邮件推荐给用户。系统运行测试表明,通过个性化推荐的动态信息更加符合用户兴趣,有助于增加科研在线团队文档库动态汇总邮件的点击率,增强用户体验。     

A Graphical Model for Context-Aware Visual Content Recommendation

Existing recommender systems provide an elegant solution to the information overload in current digital libraries such as the Internet archive. Nowadays, the sensors that capture the user's contextual information such as the location and time are become available and have raised a need to personalize recommendations for each user according to his/her changing needs in different contexts. In addition, visual documents have richer textual and visual information that was not exploited by existing recommender systems. In this paper, we propose a new framework for context-aware recommendation of visual documents by modeling the user needs, the context and also the visual document collection together in a unified model. We address also the user's need for diversified recommendations. Our pilot study showed the merits of our approach in content based image retrieval.