Design and implementation of user information sharing system using location-based services for social network services

Internet takes a role as a place for communication between people beyond a space simply for the acquisition of information.Recently,social network service(SNS)reflecting human’s basic desire for talking and communicating with others is focused on around the world.And location-based service(LBS)is a service that provides various life conveniences like improving productivity through location information,such as GPS and WiFi.This paper suggests an application combining LBS and SNS based on Android OS.By using smart phone which is personal mobile information equipment,it combines location information with user information and SNS so that the service can be developed.It also maximizes sharing and use of information via twit based on locations of friends.This proposed system is aims for users to show online identity more actively and more conveniently.

A Google Map Based Social Network (GMBSN) for Exploring Information about a Specific Territory

Social Networking is a harbinger to a more recent era in the area of computing where allocated and central resources are used in an exclusive manner. Millions of people around the globe with access to the internet are part of one or more social networks. They have permanent online accounts on Facebook and Twitter etc. where they create profiles, share photos, videos, useful links, their thoughts and spend hours catching up with what their friends are doing in their lives. The problem arise when somebody needs specific information about any city inside a country e.g. Where he/she can live? What he/she can eat? Where is the best place for outing? What are the special events relevant to that region? And may be any other help? In this paper we suggest a social network called Google map based social network (GMBSN), where users can choose their desired city of interest from the list. The selected city will be highlighted on Google map. After choosing any city from the map, the user will be able to select any category from the list and start finding and sharing information about the desired city of any country.

LBSN中基于社区联合聚类的协同推荐方法

近年来,异质网络中的社区发现逐渐成为人们关注的研究热点,然而现有大多数非重叠或重叠的社区发现方法都局限于考虑单一类型的网络结构,而无法适用于包含多模实体及其多维关系的异质网络,基于位置的社交网络(location based social network,LBSN)作为最近兴起的一种新型异质网络,如何有效发现其含有多维关系的复杂社区结构对现有研究来说是一个挑战性的难题.为此,提出了一种融合用户与位置实体及其多维关系的社区发现方法MRNMF(multi-relational nonnegative matrix factorization),该方法通过建立基于非负矩阵分解的联合聚类目标函数,并考虑融入用户社交关系、用户位置签到关系以及兴趣点特征等多维度的影响因素,能同时获得紧密关联的用户模糊社区与兴趣点聚簇结构,以有效缓解推荐中的数据稀疏问题.在2种真实LBSN数据集上的实验结果表明,所提出的MRNMF方法同时在兴趣点与朋友这双重推荐上比其他传统方法具有更优越的推荐性能.

基于改进随机森林算法的LBSN用户短期位置预测模型

针对已有的基于LBSN的用户短期位置预测模型性能较低的现状,提出了一种基于改进随机森林算法的LBSN用户短期位置预测模型SPMLIRFA.该模型对随机森林算法进行改进并应用于LBSN用户在短期内的位置预测问题中,SPMLIRFA模型的主要思想是将用户的位置预测问题抽象为对给定的候选位置的分类问题来实现.该模型将时间因素,空间因素,个人社交因素和社交群体的签到地点热门因素特征进行量化,通过计算特征量化值的Fisher比值来衡量特征的重要程度,训练样本则按照特征重要程度划分的比例来采样,再将该样本作为随机森林的训练集,生成模型后分类预测位置.实验结果表明,SPMLIRFA在用户短期位置预测问题上有着较好的泛化性和准确率.

基于LBSNS的社交传播模式及其前景分析

移位服务(LBS)与社交网站(SNS)的结合实现了"位置"与"社交"的交集,不仅能够为用户提供更加个性化服务和体验,同时也为社交网络的未来运营模式提供新的思路。本文基于移位服务(LBS)网站和网络社交服务(SNS)网站各自的发展状况,对移动社交网络服务(LBSNS)的传播模式和传播功能进行详细分析,并利用传播学原理探究LBSNS模式在传播过程中的特点,以期为LBSNS未来传播模式及其商业价值的开发提供有益借鉴。

Co-occurrence prediction in a large location-based social network

Location-based social network （LBSN） is at the forefront of emerging trends in social network services （SNS） since the users in LBSN are allowed to ＂check-in＂ the places （locations） when they visit them. The accurate geographi- cal and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the spatio-temporal co- occurrences and social ties, and the results show that the co- occurrences are strongly correlative with the social ties. Sec- ond, we present a study of predicting two users whether or not they will meet （co-occur） at a place in a given future time, by exploring their check-in habits. In particular, we first intro- duce two new concepts, bag-of-location and bag-of-time-lag, to characterize user＇s check-in habits. Based on such bag rep- resentations, we define a similarity metric called habits sim- ilarity to measure the similarity between two users＇ check-in habits. Then we propose a machine !earning formula for pre- dicting co-occurrence based on the social ties and habits sim- ilarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method.

Time and Location Aware Points of Interest Recommendation inLocation-Based Social Networks

The wide spread of location-based social networks brings about a huge volume of user check-in data, whichfacilitates the recommendation of points of interest （POIs）. Recent advances on distributed representation shed light onlearning low dimensional dense vectors to alleviate the data sparsity problem. Current studies on representation learningfor POI recommendation embed both users and POIs in a common latent space, and users＇ preference is inferred basedon the distance/similarity between a user and a POI. Such an approach is not in accordance with the semantics of usersand POIs as they are inherently different objects. In this paper, we present a novel translation-based, time and locationaware （TransTL） representation, which models the spatial and temporal information as a relationship connecting users andPOIs. Our model generalizes the recent advances in knowledge graph embedding. The basic idea is that the embedding ofa 〈time, location〉 pair corresponds to a translation from embeddings of users to POIs. Since the POI embedding shouldbe close to the user embedding plus the relationship vector, the recommendation can be performed by selecting the top-kPOIs similar to the translated POI, which are all of the same type of objects. We conduct extensive experiments on tworeal-world data.sets. The results demonstrate that our TransTL model achieves the state-of-the-art performance. It is alsomuch more robust to data sparsity than the baselines.

LBSN中结合相遇和拓扑结构的朋友推荐算法

利用基于位置的社交网络(LBSN)中的共享位置信息,提出一种结合相遇和拓扑结构的朋友推荐算法.该方法定义用户关系为相遇模型,通过考虑朋友间时间和空间的相遇特征,基于随机路点模型计算轨迹间在相同时间下的相遇频率作为用户相似度,结合拓扑相似度作为最终的推荐依据.实验结果表明,所提出的方法较传统基于拓扑的好友推荐算法准确率更高.

Exploiting Geo-Social Correlations to Improve Pairwise Ranking for Point-of-Interest Recommendation

Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user's preferences very accurately, because the users are most concerned with the list of ranked point-of-interests(POIs) on the actual output of recommender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model(GSBPR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users' geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.

iTourSPOT:a context-aware framework for next POI recommendation in location-based social networks

The rising prosperity of Location-based Social Networks(LBSNs)witnessed an explosion in the availability of geo-tagged social media data,which enables tremendous location-aware online services,especially next point of interest(POI)recommendation.However,previous next POI recommendation studies usually adopt fix-length time windows for user check-in sequence modeling,leading to a limited capacity in capturing fine-grained user temporal preferences that easily change over time.Besides,existing methods often directly leverage multi-modal contexts as auxiliary to alleviate the data sparsity issue,which fails to fully exploit the sequential patterns of contextual information for inferring user interest drift.To address the above challenges,we propose a novel framework named iTourSPOT which extends traditional collaborative filtering methods with a context-aware POI embedding architecture.For enhancing temporal interests modeling capacity,we associate the context feature extraction with varying-length sessions and incorporate check-in frequencies of POIs as prior knowledge to instruct the session representation learning of our model.Moreover,a collaborative sequence transduction model is designed for joint context sequence modeling and session-based POI recommendation.Experimental results on a real-world geo-tagged photo dataset clearly demonstrate the effectiveness of the proposed framework when compared with state-of-the-art baseline methods,especially in both sparse and cold-start scenarios.

基于地理偏好排序的兴趣点混合推荐模型

随着基于位置的社交网络(LBSN)迅速发展,作为缓解信息过载的有效手段,兴趣点(POI)推荐备受关注。由于用户签到数据是隐式反馈数据,且十分稀疏,为了有效地从用户签到数据中捕获用户POI偏好,提出了一个基于地理偏好排序的POI混合推荐模型。首先,考虑用户签到数据的隐式反馈特性及用户活动的空间约束,利用传统贝叶斯个性化排序(BPR)模型计算POI距离对POI排序的影响,提出加权BPR(GWBPR)模型;然后,针对用户签到数据的稀疏性,融合GWBPR模型和逻辑矩阵分解(LMF)模型,提出混合模型GWBPR-LMF。在两个真实数据集Foursquare和Gowalla上的实验结果表明,GWBPR-LMF模型的性能优于BPR、LMF、SAE-NAD(Self-AttentiveEncoderand Neighbor-Aware Decoder)等对比模型。与较优的对比模型SAE-NAD相比,GWBPR-LMF模型的POI推荐的精确率、召回率、F1值、平均精度均值(mAP)、归一化折损累积增益(NDCG)在数据集Foursquare上分别平均提升了44.9%、57.1%、78.4%、55.3%和40.0%,在数据集Gowalla上分别平均提升了3.0%、6.4%、4.6%、11.7%和4.2%。

在线社交网络中的位置服务研究进展与趋势

在基于位置的社交网络中,用户通过发布嵌入了位置数据的媒体信息获得服务,如位置或好友推荐、旅行路线推荐等。用户和位置都是网络的主体,位置作为桥梁将用户的虚拟网络世界与现实世界联系起来。综述了基于位置的在线社交网络中的位置信息获取、用户识别、位置识别、信息的共享与传播及位置隐私的相关研究,预测了基于位置的在线社交网络未来的研究趋势。

基于Ranking的泊松矩阵分解兴趣点推荐算法

随着基于位置社交网络(location-based social network,LBSN)的发展,兴趣点推荐成为满足用户个性化需求、减轻信息过载问题的重要手段.然而,已有的兴趣点推荐算法存在如下的问题:1)多数已有的兴趣点推荐算法简化用户签到频率数据,仅使用二进制值来表示用户是否访问一个兴趣点;2)基于矩阵分解的兴趣点推荐算法把签到频率数据和传统推荐系统中的评分数据等同看待,使用高斯分布模型建模用户的签到行为;3)忽视用户签到数据的隐式反馈属性.为解决以上问题,提出一个基于Ranking的泊松矩阵分解兴趣点推荐算法.首先,根据LBSN中用户的签到行为特点,利用泊松分布模型替代高斯分布模型建模用户在兴趣点上签到行为;然后采用BPR(Bayesian personalized ranking)标准优化泊松矩阵分解的损失函数,拟合用户在兴趣点对上的偏序关系;最后,利用包含地域影响力的正则化因子约束泊松矩阵分解的过程.在真实数据集上的实验结果表明:基于Ranking的泊松矩阵分解兴趣点推荐算法的性能优于传统的兴趣点推荐算法.

基于位置社会网络的双重细粒度兴趣点推荐

兴趣点推荐是在基于位置社会网络(location-based social network,LBSN)中流行起来的一种全新形式的推荐.利用LBSN所包含的丰富信息进行个性化推荐能有效增强用户体验和提高用户对LBSN的依赖度.针对无显示用户偏好、兴趣非一致性和数据稀疏性等挑战性问题,研究一种针对LBSN的双重细粒度POI推荐策略,即一方面将用户的全部历史签到信息以小时为单位细分为24个时间段,另一方面将每个POI细分为多个潜在主题及其分布,同时利用用户的历史签到信息和评论信息挖掘出用户在不同时间段的主题偏好,以实现POI的Top-N推荐.为实现该推荐思路,首先,根据用户的评论信息,运用LDA模型提取出每个POI的主题分布;然后,对于每个用户,将其签到信息划分到24个时间段中,通过连接相应的POI主题分布映射出用户在不同时间段对每个主题的兴趣偏好.为解决数据稀疏问题,运用高阶奇异值分解算法对用户-主题-时间三阶张量进行分解,获取用户在每个时间段对每个主题更为准确的兴趣评分.在真实数据集上进行了性能测试,结果表明所提出的推荐策略具有较好的推荐效果.

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。

多因素融合的个性化位置推荐算法

位置推荐中影响推荐结果的主要因素有地理位置、个人爱好、社会关系以及时间周期,为有效融合4个影响因素并进行个性化位置推荐,针对每个因素构建对应的选择概率模型,并分析各因素对用户选择的影响力,在此基础上,提出一种启发式推荐算法。实验结果表明,与传统的基于位置的推荐算法相比,该算法性能更好,推荐的结果更能被用户所接受。

一种基于多类型情景信息的兴趣点推荐模型

当前最新的兴趣点推荐工作开始融合地理、文本和社交信息进行推荐,但是还存在信息挖掘不充分的情况。为此,提出了改进的多类型信息融合的联合概率生成的兴趣点推荐模型。首先提出了自动学习文档话题数目的分层狄利克雷过程主题模型,学习用户和兴趣点相关兴趣话题;同时,利用由签到分布决定带宽大小的核密度估计法,个性化地理信息对用户签到行为的影响,而且还融合了用户位置访问序列中已访问兴趣点对待访问兴趣点的影响,即序列模式的影响;然后综合考虑了用户社交关系的影响;最后基于联合概率生成模型,融合文本、地理、社会和序列信息,提出TGSS-PGM兴趣点推荐模型,依据计算结果从而生成兴趣点推荐列表推荐给用户。实验结果表明,该模型在推荐准确率等多种评价指标上都取得了更好的结果。

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。

基于位置的社交网络中基于时空关系的超网络链接预测方法

针对基于位置的社交网络(LBSN)中因现有方法未能有效融合社会因素、位置因素以及时间因素的综合影响而导致链接预测准确度低的问题,提出了一种LBSN中基于时空关系的超网络链接预测方法。首先,针对LBSN中网络的异构性以及用户间的时空关系特性,将网络划分成"时空-用户-位置-类别"四层超网络,降低影响因素间的耦合性;其次,考虑到边权值对网络的影响,通过挖掘用户影响力、隐关联关系、用户偏好以及节点度信息,对子网的边权值进行定义和量化,构建四层加权超网络模型;最后,在加权超网络模型的基础上,定义超边及加权超边结构,挖掘用户之间的多元关联关系进行预测。实验结果表明,所提方法较基于同构和异构的链接预测方法在准确率、召回率、F1值以及AUC上具有一定的提升,其中AUC指标较基于异构的链接预测方法提升了4.69%。