融合深度神经网络和张量分解的地点推荐算法

为提高利用张量分解技术进行基于位置社交网络的地点推荐的推荐性能,提出一种利用张量分解技术且融合神经网络的地点推荐算法。融合多层感知机和长短期记忆网络基于张量分解技术建模用户的签到行为,将学习到的用户偏好表示馈送到推荐生成器和推荐判别器组成的对抗生成网络中,通过对抗训练学习最佳用户偏好表示用于推荐。基于真实数据集的实验验证了该算法的有效性和高效性。     

基于用户偏好特征挖掘的个性化推荐算法

为了提升社交网络个性化推荐能力,结合用户行为分布进行个性化推荐设计,文中提出基于用户行为特征挖掘的个性化推荐算法,构建社交网络的用户行为信息特征挖掘模型,采用显著数据分块检测方法对社交网络用户特征的行为信息进行融合处理,提取反映用户偏好的语义信息特征量。从情感、关键词和结构等方面根据用户行为特征组,结合模糊信息感知方法进行社交网络个性化推荐过程中的信息融合处理,在关联规则约束控制下,构建社交网络用户偏好特征的混合推荐模型,实现用户偏好特征挖掘,根据语义分布和用户的行为偏好实现社交网络的个性化信息推荐。仿真结果表明,采用所提方法进行社交网络个性化推荐的特征分辨能力较好,对用户行为特征的准确识别能力较强,提高了社交网络推荐输出的准确性。     

融合社交关系与时间因素的主题模型推荐算法

用户的行为偏好往往会受到社交关系、时间变化等多种因素影响,只考虑单一因素会导致构建的用户兴趣模型比较片面,难以准确地产生推荐。为此,融合用户社交关系和时间因素,提出一种主题模型推荐算法。利用主题模型对用户标注行为进行主题建模,得到用户-物品概率矩阵。根据用户标注物品的时间计算用户标注行为的时间权重,将其与用户的标注行为权重相结合,计算基于时间的用户相似度。对用户的社交关系与基于时间的用户相似度进行加权处理得到用户的权重,在此基础上,考虑其他用户的影响,计算用户对物品最终的偏好权重,并根据排名产生推荐结果。在Last.fm数据集上的实验结果表明,该算法能更全面地考虑用户特征,有效提高推荐的质量。     

基于用户空间位置评分云模型的Web服务协同过滤推荐算法

Web服务作为无形的产品,不具备真实环境下的空间地理位置坐标,针对服务推荐中无法衡量用户群体与Web服务之间的距离位置关系,造成用户相似度计算失衡,导致推荐不准确等问题,提出了基于用户空间位置评分云模型的Web服务协同过滤推荐算法.首先基于用户群体的行为数据量化Web服务的热度区域,通过空间位置量化评分描述用户对于Web服务的兴趣偏好;其次利用云模型来描述每个用户空间行为评分的整体特征,设计了云模型间相似贴近度的计算方法,基于该方法提出了一种用户差异程度系数评估算法,并作为调控系数优化了皮尔森相似度量;最后通过协同过滤找出用户感兴趣的Web服务.实验结果表明该算法使得用户行为偏好的区域划分更加精确,在推荐准确率上明显提高,为基于位置的Web服务推荐提供新颖的方案.     

SocoTraveler: Travel-package recommendations leveraging social influence of different relationship types

The immense amount of data generated and collected on e-commerce platforms provides opportunities and challenges for big data analytics to create business value. E-tourism platforms collect not only users’ travel information but also users’ social connection information and need effective personalized recommendation systems for target marketing. In this paper, we aim to study how different types of social relationships such as colleague, schoolmate, and relative between co-travelers influence a user’s travel behavior and how to use this influence to enhance recommendation quality. To this end, we develop a probabilistic topic model leveraging individual travel history and social influence of co-travelers to capture personal interests and propose a recommendation method to utilize the proposed model. Experiments on a real travel dataset show that the proposed approach significantly outperforms benchmarks. The result highlights useful findings for travel agencies.     

基于语义位置和区域划分的兴趣点推荐模型

针对现有的位置社交网络研究工作对兴趣点相关的用户语义位置信息挖掘不够充分,且大多推荐算法忽略了兴趣点所在区域对推荐结果的影响,提出了一种新型兴趣点推荐模型（USTTGD）。首先采用分割时间的潜在狄利克雷分配主题模型（latent Dirichlet allocation,LDA）,基于签到记录中的语义位置信息挖掘时间主题下的用户时间偏好,然后将兴趣点所处区域划分为网格,以评估区域影响;接着应用边缘加权的个性化PageRank（edge-weighted personalized PageRank,EwPPR）来建模兴趣点之间的连续过渡;最后将用户时间偏好、区域偏好和连续过渡偏好融合为一个统一的推荐框架。通过在真实数据集上实验验证,与其他传统推荐模型相比,USTTGD模型在准确率和召回率上有了显著的提升。     

基于LBSN的个性化旅游包推荐系统

旅游是人们生活中的重要部分,但是制定旅游计划是一件繁杂的工作。基于位置的社交网络(LBSN)的发展,提供了大量关于位置和活动的信息。为辅助用户制订旅游计划,本文提出一个基于LBSN的个性化旅游包推荐系统。该系统利用采集自LBSN的数据,建立地点和用户偏好模型,根据用户需求,在时空约束下生成旅游路线,形成旅游包推荐给用户。本文实现的原型系统能交互地获取用户旅游意向,实时生成多个旅游包供用户选择,对游客制订旅游计划具有一定的参考价值。     

A tensor decomposition based collaborative filtering algorithm for time-aware POI recommendation in LBSN

Point of interest (POI) recommendation problem in location based social network (LBSN) is of great importance and the challenge lies in the data sparsity, implicit user feedback and personalized preference. To improve the precision of recommendation, a tensor decomposition based collaborative filtering (TDCF) algorithm is proposed for POI recommendation. Tensor decomposition algorithm is utilized to fill the missing values in tensor (user-category-time). Specifically, locations are replaced by location categories to reduce dimension in the first phase, which effectively solves the problem of data sparsity. In the second phase, we get the preference rating of users to POIs based on time and user similarity computation and hypertext induced topic search (HITS) algorithm with spatial constraints, respectively. Finally the user’s preference score of locations are determined by two items with different weights, and the Top-N locations are the recommendation results for a user to visit at a given time. Experimental results on two LBSN datasets demonstrate that the proposed model gets much higher precision and recall value than the other three recommendation methods.

     

Intelligent system applications in electronic tourism

Recent changes in tourists’ behavior and the growing importance of Information and Communication Technologies mean that much more attention needs to be given to electronic (e)-tourism. With the Internet becoming the preferred media choice for many travelers to obtain travel information, online travel agencies and their offers are gaining more importance all over the world, also in Turkey. Turkish travel web sites usually provide static information which can be accessed by some kind of search forms, but users need more such as recommendation tools, trip planners which include a Decision Support System inside. For this reason, this paper offers a new approach to the marketing strategies for Turkish travel agencies, but the recommendation tool is also a generic model for all tourism agencies in the world. An intelligent system which works as a recommendation tool for trip planning is created by using Case based reasoning algorithm. The inspiration module of the proposed model recommends users the most available trip alternatives by comparing the older cases with the new client.     

基于改进的倾向得分估计的无偏推荐模型

现实中推荐系统通常遭受着各种各样的偏置问题,例如曝光偏置、位置偏置和选择偏置。一个忽略偏置问题的推荐模型不能反映推荐系统的真实性能,且对于用户而言可能是不可信任的。先前的工作已经表明基于倾向得分估计的推荐模型能够有效缓解隐式反馈数据的曝光偏置,但是通常只考虑通过物品信息来估计倾向得分,这可能导致倾向得分估计不准确。为了提高倾向得分估计的准确性,提出配对倾向得分估计（MPE）方法。具体来说,该方法引入了用户流行度偏好的概念,通过计算用户流行度偏好和物品流行度的配对程度来对样本曝光率进行更加精确的建模,最后将提出的估计方法和一个主流的传统推荐模型以及一个无偏推荐模型进行集成并和包括前两者的三个基线模型进行对比。在公开数据集上的实验结果表明,结合MPE方法后的模型分别相比对应的基线模型在召回率、折损累计增益（DCG）和平均准确率（MAP）这三个评估指标上均有显著的提升;此外,通过实验结果还观察到性能的增益有很大一部分来自长尾物品,可见所提方法有助于提升推荐物品的多样性与覆盖率。     

Dynamic User Equilibrium Model for Combined Activity-Travel Choices Using Activity-Travel Supernetwork Representation

Integrated urban transportation models have several benefits over sequential models including consistent solutions, quicker convergence, and more realistic representation of behavior. Static models have been integrated using the concept of Supernetworks. However integrated dynamic transport models are less common. In this paper, activity location, time of participation, duration, and route choice decisions are jointly modeled in a single unified dynamic framework referred to as Activity-Travel Networks (ATNs). ATNs is a type of Supernetwork where virtual links representing activity choices are added to augment the travel network to represent additional choice dimensions. Each route in the augmented network represents a set of travel and activity arcs. Therefore, choosing a route is analogous to choosing an activity location, duration, time of participation, and travel route. A cell-based transmission model (CTM) is embedded to capture the traffic flow dynamics. The dynamic user equilibrium (DUE) behavior requires that all used routes (activity-travel sequences) provide equal and greater utility compared to unused routes. An equivalent variational inequality problem is obtained. A solution method based on route-swapping algorithm is tested on a hypothetical network under different demand levels and parameter assumptions.     

基于用户隐性反馈行为的下一个购物篮推荐

下一个购物篮推荐是当前电子商务领域中极其重要的一项任务,传统的下一个购物篮推荐方法主要分为时序推荐模型和总体推荐模型。这些方法对点击、收藏、加入购物车等用户的隐性反馈行为利用得不够,并且没有考虑用户行为偏好的时间敏感性。该文提出了一种基于用户隐性反馈行为的下一个购物篮推荐方法,将用户行为按照一定的时间窗口进行划分,对于每个窗口从多个维度抽取用户对商品的时序偏好特征,运用深度学习领域的卷积神经网络模型进行分类器训练。在真实数据集中的实验结果表明,与传统的线性模型和树模型等分类器相比,该文提出的卷积神经网络框架具有较强的特征萃取能力和泛化能力,提高了推荐系统的用户满意度。     

Exploiting interactions of review text,hidden user communities and item groups,and time for collaborative filtering

Rich side information concerning users and items are valuable for collaborative filtering (CF) algorithms for recommendation. For example, rating score is often associated with a piece of review text, which is capable of providing valuable information to reveal the reasons why a user gives a certain rating. Moreover, the underlying community and group relationship buried in users and items are potentially useful for CF. In this paper, we develop a new model to tackle the CF problem which predicts user’s ratings on previously unrated items by effectively exploiting interactions among review texts as well as the hidden user community and item group information. We call this model CMR (co-clustering collaborative filtering model with review text). Specifically, we employ the co-clustering technique to model the user community and item group, and each community–group pair corresponds to a co-cluster, which is characterized by a rating distribution in exponential family and a topic distribution. We have conducted extensive experiments on 22 real-world datasets, and our proposed model CMR outperforms the state-of-the-art latent factor models. Furthermore, both the user’s preference and item profile are drifting over time. Dynamic modeling the temporal changes in user’s preference and item profiles are desirable for improving a recommendation system. We extend CMR and propose an enhanced model called TCMR to consider time information and exploit the temporal interactions among review texts and co-clusters of user communities and item groups. In this TCMR model, each community–group co-cluster is characterized by an additional beta distribution for time modeling. To evaluate our TCMR model, we have conducted another set of experiments on 22 larger datasets with wider time span. Our proposed model TCMR performs better than CMR and the standard time-aware recommendation model on the rating score prediction tasks. We also investigate the temporal effect on the user–item co-clusters.     

基于TF-IQF模型的查询建议

基于TF-IQF模型的建议方法不考虑用户查询行为的上下文,在满足用户个性化需求方面存在缺陷。针对这一情况,在该方法的基础上进行优化改进,根据不同用户的查询上下文来分析用户的查询偏好,重新排序系统推荐的查询。实验结果表明,改进方法能够给出个性化的查询建议,提高用户查询的满意度。     

基于用户兴趣和兴趣点流行度的个性化旅游路线推荐

针对传统的旅游路线推荐算法推荐准确率不高的缺陷,提出一种基于兴趣点(POI)流行度和用户兴趣偏好的个性化旅游路线推荐(PTIR)算法。首先通过分析得到用户真实的历史旅游足迹;然后根据用户在每个景点的逗留时间提出基于时间的用户兴趣偏好;最后在给定的旅行时间限制、起点和终点下,设计最优旅游路线计算方法。在Flickr社交网站的真实数据集上进行实验,结果显示,相比传统的只考虑POI流行度的算法,该个性化旅游路线推荐算法的准确率和召回率都有较大提升;相比只考虑用户兴趣偏好的算法,该个性化旅游路线推荐算法的准确率和召回率也有所提高。实验结果表明综合考虑POI流行度和用户兴趣偏好能使路线推荐得更准确。     

一种社会化标注系统资源个性化推荐方法

目前许多基于社化化标注的个性化资源推荐方法均忽视了用户长短期兴趣和多义标签问题对推荐的不同影响,为此,设计区分用户长短期兴趣的指标——用户的标签偏好权重和资源偏好权重;在此基础上,提出一种结合基于内容和基于协同过滤方法优点的混合推荐方法,通过加入标注相同资源的标签向量相似度计算因子,来减小多义标签对推荐结果的影响。实验表明,将该方法引入社会化标注系统资源个性化推荐算法中,能提高推荐精度。     

User-adapted travel planning system for personalized schedule recommendation

Recently, the Internet has made a lot of services and products appear online provided by many tourism sectors. By this way, many information such as timetables, routes, accommodations, and restaurants are easily available to help travelers plan their travels. However, how to plan the most appropriate travel schedule under simultaneously considering several factors such as tourist attractions visiting, local hotels selecting, and travel budget calculation is a challenge. This gives rise to our interest in exploring the recommendation systems with relation to schedule recommendation. Additionally, the personalized concept is not implemented completely in most of travel recommendation systems. One notable problem is that they simply recommended the most popular travel routes or projects, and cannot plan the travel schedule. Moreover, the existing travel planning systems have limits in their capabilities to adapt to the changes based on users’ requirements and planning results. To tackle these problems, we develop a personalized travel planning system that simultaneously considers all categories of user requirements and provides users with a travel schedule planning service that approximates automation. A novel travel schedule planning algorithm is embedded to plan travel schedules based on users’ need. Through the user-adapted interface and adjustable results design, users can replace any unsatisfied travel unit to specific one. The feedback mechanism provides a better accuracy rate for next travel schedule to new users. An experiment was conducted to examine the satisfaction and use intention of the system. The results showed that participants who used the system with schedule planning have statistical significant on user satisfaction and use intention. We also analyzed the validity of applying the proposed algorithm to a user preference travel schedule through a number of practical system tests. In addition, comparing with other travel recommendation systems, our system had better performance on the schedule adjustment, personalization, and feedback giving.     

一种融合多因素社交活动个性化推荐模型

针对当前网络社交活动个性化推荐精度较低的问题,融合用户对活动兴趣度、召集者影响力以及地理位置偏好等三方面因素,提出一种融合多因素社交活动个性推荐模型。采用LDA文件主题模型求取用户与其参加过的所有社交活动的主题分布,利用隐含主题概率分布来表征用户的兴趣度,并构建用户与召集者间的影响力矩阵。根据活动举办地与用户常住地,建立距离幂律分布,并结合用户参加活动的频数,建立用户地理位置偏好概率模型。采用不同权值配比,综合三方面的因素形成最终的社交活动个性推荐。对比实验表明,该算法与三个因素个性推荐算法相比,准确率至少提高了36.7%,召回率至少提高了35.9%;与其他两个同类网络社交活动推荐算法相比准确率至少提高了8.77%,召回率至少提高了8.57%。     

基于多重隐语义表示模型的旅游路线挖掘

针对用户个性化旅游行为过程的挖掘与景点推荐问题,提出多重隐语义旅游路线表示模型(MLSTR-RM).MLSTR-RM考虑不同上下文对用户旅游路线的影响,高效挖掘旅游路线中丰富的隐语义.首先确定模型中不同上下文包含的隐语义信息,然后通过负采样的方式训练模型参数,最后基于MLSTR-RM模型设计个性化景点推荐方法.在真实数据集上的实验表明文中模型的有效性.