基于人物角色的产品用户需求获取方法研究

为了使产品设计能深入满足用户的真实需求,在设计开发前期有效准确获取用户需求就变得十分关键。提出了一种基于人物角色模型的产品用户需求获取的新方法。通过场景细分确定典型用户,结合5W2H法提炼构建用户人物角色模型,通过KJ法对人物角色模型中提炼的用户需求进行聚类,得到典型用户需求指标体系。使用改进后的主观赋权法确定参数的权重,加权各方案并比较最终确定用户需求。并以无线移动终端产品造型设计为例进行说明。     

角色模型的粗集-模糊聚类分析方法

角色分析可以满足产品个性化设计系统中对于用户模型构建的需要。提出了基于粗集的模糊聚类角色分析与模型构建方法,通过构造基于粗集的模糊相似矩阵、确定角色属性的模糊相似聚类分析方法,从用户调研数据中提取典型用户属性特征,构建角色模型。该方法完善了角色分析在产品设计中的应用方法,有助于快速生成概念产品设计模型与方案。     

Present-ing the user: constructing the persona

Abstract

During the last few years personas has become an established design technique within the IT-design field. Using personas has proven itself as a valuable approach for designers to switch between a developer's perspective and a user's perspectivein the design process. The technique is claimed to help designers in keeping a clear focus and shaping a consistent user-interface by making ‘the user’ present in the design work. In this paper we report on a number of projects where we have elaborated on the persona approach for collaborative design. With the goalof creating ‘user presence’ in the design process, we have developed an approach building on a combination of ethnographic exploration, participatory inquiry, and collaborative design. This paper carries two interrelated points: the grounding of personasin existing practice; and the notion that ‘the user’ is created as an ongoing process throughout the design work.     

Multi-valued collaborative QoS prediction for cloud service via time series analysis

Aiming at the diversity of user features, the uncertainty and the variation characteristics of quality of service (QoS), by exploiting the continuous monitoring data of cloud services, this paper proposes a multi-valued collaborative approach to predict the unknown QoS values via time series analysis for potential users. In this approach, the multi-valued QoS evaluations consisting of single-value data and time series data from consumers are transformed into cloud models, and the differences between potential users and other consumers in every period are measured based on these cloud models. Against the deficiency of existing methods of similarity measurement between cloud models, this paper presents a new vector comparison method combining the orientation similarity and dimension similarity to improve the precision of similarity calculation. The fuzzy analytic hierarchy process method is used to help potential users determine the objective weight of every period, and the neighboring users are selected for the potential user according to their comprehensive similarities of QoS evaluations in multiple periods. By incorporating the multi-valued QoS evaluations with the objective weights among multiple periods, the predicted results can remain consistent with the periodic variations of QoS. Finally, the experiments based on a real-world dataset demonstrate that this approach can provide high accuracy of collaborative QoS prediction for multi-valued evaluations in the cloud computing paradigm.     

基于高斯pLSA模型与项目的协同过滤混合推荐

协同过滤是推荐系统中常用的一种技术。以往的推荐算法往往只从用户或商品的角度单一地进行推荐,在推荐准确率上存在瓶颈和局限性。提出了一种新的混合推荐方法——结合基于高斯概率潜在语义分析模型与改进的基于项目的协同过滤算法,通过建立用户群体混合模型和基于目标项目的邻居集进行预测推荐。实验证明该算法与其他协同过滤算法相比具有更高的准确率。     

A Qualitative Design Approach for Exploring the Use of Medication and Health Care Devices among Elderly Persons

An academia–industry collaborative research project regarding design for elderly persons was initiated to investigate their living needs and potential design opportunities for new technologies and products. In the first year, a qualitative design approach for exploring their use of medication and health care devices was proposed. First, a user study was conducted using self‐reporting, observation, and interview methods. Four personas representing different lifestyle patterns of elderly persons were then derived from the user study data. Finally, four designers were invited to present and synthesize their design ideas for those personas. Results showed that the user study could reveal considerable information and that the persona method was effective for designers to communicate their ideas and concentrate on user requirements. The study findings suggested that, for design for elderly persons, social and affective factors can be considered with decline in age‐related abilities. © 2012 Wiley Periodicals, Inc.     

Spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth in Internet of Things

The temporal and spatial characteristics of users are involved in most Internet of Things (IoT) applications. The spatial and temporal movement patterns of users are the most direct manifestation of the temporal and spatial characteristics. The user’s interests, activities, experience and other characteristics are reflected by mobile mode. In view of the low clustering efficiency of moving objects in convergent pattern mining in the IoT, a spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth is proposed. Based on the temporal characteristics of user trajectories, frequent and asynchronous periodic spatiotemporal movement patterns are mined. Firstly, the location sequence is modeled, and the time information is added to the model. Then, a mining algorithm of asynchronous periodic sequential pattern is adopted. The algorithm is based on multiple minimum supports of pattern growth. According to multiple minimum supports, the sequential pattern of asynchronous period is mined deeply and recursively. Finally, the proposed method is validated and evaluated by Gowalla dataset, in which the user characteristics are truly reflected. It is shown by the experimental results that the average pointwise mutual information (PWI) of the proposed algorithm reaches 0.93. And the algorithm is proved to be effective and accurate.

     

Recommender system with grey wolf optimizer and FCM

Recommender systems are contributing a significant aspect in information filtering and knowledge management systems. They provide explicit and reliable recommendations to the users so that user can get information about all products in e-commerce domain. In the era of big data and large complex information delivery system, it is impossible to get the right information in the online environment. In this research work, we offered a novel movie-based collaborative recommender system which utilizes the bio-inspired gray wolf optimizer algorithm and fuzzy c-mean (FCM) clustering technique and predicts rating of a movie for a particular user based on his historical data and similarity of users. Gray wolf optimizer algorithm was applied on the Movielens dataset to obtain the initial clusters, and also the initial positions of clusters are obtained. FCM is used to classify the users in the dataset by similarity of user ratings. Our proposed collaborative recommender system performed extremely well with respect to accuracy and precision. We analyzed our proposed recommender system over Movielens dataset which is available publically. Various evaluation metrics were utilized such as mean absolute error, standard deviation, precision and recall. We also compared the performance of projected system with already established systems. The experiment results delivered by proposed recommender system demonstrated that efficiency and performance are enhanced and also offered better recommendations when compared with our previous work [1].

     

Hybrid recommendation approaches for multi-criteria collaborative filtering

Recommender systems are software tools and techniques for suggesting items in an automated fashion to users tailored their preferences. Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a user’s needs from the neighborhoods of like-minded people, are becoming increasingly popular as ways to overcome the information overload. The multi-criteria based CF presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects and several methods have been proposed for improving the accuracy of these systems. However, the problem of multi-criteria recommendations with a single and overall rating is still considered an optimization problem. In addition, increasing the accuracy in predicting the appropriate items tailored to the users’ preferences is on of the main challenges in these systems. Hence, in this research new recommendation methods using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Self-Organizing Map (SOM) clustering are proposed to improve predictive accuracy of criteria CF. In this research, SOM enables us to generate high quality clusters of dataset and ANFIS is used for discovering knowledge (fuzzy rules) from users’ ratings in multi-criteria dataset, generating appropriate membership functions (MFs), overall rating prediction and input selection. Using exhaustive search method for input selection, the effective inputs are determined to build the ANFIS models in all generated clusters. Furthermore, new fuzzy-based algorithms, Weighted Fuzzy MC-CF (WFuMC-CF), Fuzzy Euclidean MC-CF (FuEucMC-CF) and Fuzzy Average MC-CF (FuAvgMC-CF), are presented for prediction task in multi-criteria CF. FuEucMC-CF and FuAvgMC-CF algorithms uses the fuzzy-based Euclidian distance and fuzzy-based average similarity, respectively, the WFuMC-CF algorithm uses fuzzy-based user- and item-based prediction in a weighted approach. Experimental results on real-world dataset demonstrate that the proposed hybrid methods remarkably improve the accuracy of multi-criteria CF in relation to the previous methods based on multi-criteria ratings.     

电子商务推荐系统中推荐策略的自适应性

针对电子商务推荐系统中各种推荐技术的不足,提出推荐策略的自适应方法。用二元组《用户知识,推荐商品》代表推荐环境的根本特征．采用ART神经网络进行自学习,获取推荐环境的不同聚类。每个聚类代表了某种推荐环境,对推荐结果的反馈情况进行统计分析．确定每个聚类的最佳推荐技术。向用户推荐商品时,根据用户所在聚类采用具有最佳推荐质量的推荐技术向用户作出推荐。整个系统的工作过程不需要人工干预,具有自适应性。     

融合知识图谱和轻量图卷积网络的推荐算法

针对知识图谱推荐算法用户端和项目端建模程度不均且模型复杂度较高等问题, 提出融合知识图谱和轻量图卷积网络的推荐算法. 在用户端, 利用用户相似性生成邻居集合, 将用户及其相似用户的交互记录在知识图谱上多次迭代传播, 增强用户特征表示. 在项目端, 将知识图谱中实体嵌入传播, 挖掘与用户喜好相关的项目信息; 接着, 利用轻量图卷积网络聚合邻域特征获得用户和项目的特征表示, 同时采用注意力机制将邻域权重融入实体, 增强节点的嵌入表示; 最后, 预测用户和项目之间的评分. 实验表明, 在Book-Crossing数据集上, 相较于最优基线, AUC和ACC分别提高了1.8%和2.3%. 在Yelp2018数据集上, AUC和ACC分别提高了1.2%和1.4%. 结果证明, 该模型与其他基准模型相比有较好的推荐性能.     

基于“逐层深入”系统的e-Bed 功能定位与创意构思

为准确定位用户需求,提出了多种用户研究方法串联进行的“逐层深入” 用户研究系统,并在床、椅一体化护理平台（简称e-Bed）的项目中应用该系统研究用户, 得到需求域。进而将用户需求域提升为产品功能域,形成产品的初步功能定位及创意构思。 在该系统中,又提出并实践了基于实际场景的角色代入的用户研究方法,以求更为准确、深 入地挖掘用户需求。     

LoHDP: Adaptive local differential privacy for high-dimensional data publishing

The increasing availability of high-dimensional data collected from numerous users has led to the need for multi-dimensional data publishing methods that protect individual privacy. In this paper, we investigate the use of local differential privacy for such purposes. Existing solutions calculate pairwise attribute marginals to construct probabilistic graphical models for generating attribute clusters. These models are then used to derive low-dimensional marginals of these clusters, allowing for an approximation of the distribution of the original dataset and the generation of synthetic datasets. Existing solutions have limitations in computing the marginals of pairwise attributes and multi-dimensional distribution on attribute clusters, as well as constructing relational dependency graphs that contain large clusters. To address these problems, we propose LoHDP, a high-dimensional data publishing method composed of adaptive marginal computing and an effective attribute clustering method. The adaptive local marginal calculates any k-dimensional marginals required in the algorithm. In particular, methods such as sampling-based randomized response are used instead of privacy budget splits to perturb user data. The attribute clustering method measures the correlation between pairwise attributes using an effective method, reduces the search space during the construction of the dependency graph using high-pass filtering technology, and realizes dimensionality reduction by combining sufficient triangulation operation. We demonstrate through extensive experiments on real datasets that our LoHDP method outperforms existing methods in terms of synthetic dataset quality.     

基于用户模式聚类的智能信息推荐算法

基于数据挖掘的智能信息推荐日益成为一个重要的研究课题。针对现有智能信息推荐算法存在的不足,提出了一种基于用户模式聚类的智能信息推荐算法（IRUMC）。该算法将相似的用户模式聚类到一起,生成用户聚类模式,然后将用户访问操作与用户聚类模式进行匹配,最后形成推荐集。它比较适合新用户、访问站点较少的用户和有新颖性信息需求的用户。实验结果表明,该算法是有效的。     

An Analytical Model for Identifying Suspected Users on Twitter

Abstract

With the use of identity resolution, both information leakage and identity hacking can be reduced to some extent. In this paper, a prototype has been developed to classify Twitter users as suspicious and nonsuspicious on the basis of features which identify user demographics and their tweeting activity using Twitter APIs. A model has been devised based upon user and tweet meta-data which is used to calculate user score and tweet score, and further aggregate the values generated by these scores to label suspicious and nonsuspicious users in the collected dataset of around 21,492 Twitter users. Further, support vector machine classifier has been used to classify the labeled data. Through this paper, our analysis about the role of features and the characteristics of dataset used for the categorization of users in Twitter has been reported. The experimental results illustrate that the proposed system can identify suspicious users with an accuracy of 94.1%.     

融合领域知识的药店用户画像建模及应用

用户画像是对用户形象的勾勒与描述,现已广泛应用于睡眠会员唤醒,用户到店预测,个性化推荐等典型零售场景,药品不同于普通商品,包含较强的语义知识,现有用户画像主要从消费属性和静态属性出发,不能完全适用于药店销售和预测领域.本文提出了一种针对药品领域的用户画像模型UPP (persona of pharmacy user),在现有画像的基础上嵌入医药知识,利用规则,聚类,统计,实体识别等方法提取慢病、疾病、特殊病类、活动敏感度、用户价值、价格偏好等新标签.将所有标签融入一种基于聚类的群体划分方法,形成用户画像.实验表明,该模型相较于现有的用户画像模型,在消费行为预测场景下精准率提高了13%,更加适用于药店营销场景.     

基于区域语义的城市移动模式可视分析

针对城市区域语义及移动模式难以提取的问题,提出一种基于区域语义的城市移动模式可视分析方法用于直观地分析人群出行情况.通过提取用户通话特征,使用高斯混合模型区分基站通话模式来发现城市区域的功能性信息;进一步使用层次聚类算法对用户行为进行语义发现,分析区域用户行为规律;区域语义与用户语义结合分析,挖掘人群在区域间的移动模式.案例分析表明,该方法能有效地发现区域功能特征,结合数据能帮助分析人员发现城市间移动模式以及探索用户移动意图,得到用户移动模式和功能区域之间的联系.     

基于人物角色的用户建模研究

在以用户为中心的软件设计中,需要通过对用户建模来明确和分析用户,了解用户的特定需求.介绍用户建模的相关概念,阐述基于人物角色的用户建模原理、详细介绍基于人物角色的用户建模过程,指出人物角色在以用户为中心的软件设计过程中的应用.     

Attribute selection-based recommendation framework for short-head user group: An empirical study by MovieLens and IMDB

Most of recommender systems have serious difficulties on providing relevant services to the “short-head” users who have shown intermixed preferential patterns. In this paper, we assume that such users (which are referred to as long-tail users) can play an important role of information sources for improving the performance of recommendation. Attribute reduction-based mining method has been proposed to efficiently select the long-tail user groups. More importantly, the long-tail user groups as domain experts are employed to provide more trustworthy information. To evaluate the proposed framework, we have integrated MovieLens dataset with IMDB, and empirically shown that the long-tail user groups are useful for the recommendation process.     

最小描述长度优化下的医学图像统计形状建模

统计形状模型(SSM)是有效的图像处理与分析方法。为了建立模型,需要从形状样本集中提取出具有对应关系的轮廓采样点集合,这是决定模型性能的关键。传统的手动标定这些点集来确保对应关系枯燥耗时且带有主观性,更难以向高维拓展。对形状建立逐层的多尺度参数表示,基于最小描述长度(MDL),在粗尺度上建立反映点对应程度的目标函数并最小化,提出首先确保粗尺度上具有最优意义的点对应,同时在精尺度上使用最便捷的弧长参数函数来确定特征点,完成感兴趣目标的快速统计形状建模,进而统计分析以验证模型性能,为后续图像分割或定量分析打下基础。实验对肌肉骨骼核磁共振成像(MRI)中椎骨、椎间盘以及半月板等具有临床意义的结构建立了统计形状模型,验证了本文方法与手动取点相比具有客观可重复性且更加简洁,与单一尺度下的MDL方法相比时间效率更高。基于此模型的图像分割与基于手动建模的分割相比,误差相当或有所降低。