A method for product form design of integrating interactive genetic algorithm with the interval hesitation time and user satisfaction

Due to the continuous release of new products, manufacturers are paying attention to customer-oriented design of products that meet user needs to minimize the risk of their products being rejected by the market. Due to the ambiguity of user cognition, it is difficult to accurately obtain the user's preference for individual productions. To respond to the challenge, we propose an engineering scientific research method of interactive genetic algorithm with the interval arithmetic based on hesitation and fuzzy kano model(FKM) to explore the emotional needs of users for product forms and drive product modeling evolution design. Through expert interviews, the morphological characteristics and perceptual images factors of the products attracting users are investigated. In order to identify the user's satisfaction relationship with the perceptual images, we use FKM to analyze the product image style that meets the user's kansei needs accurately and selects 5 factors which is attractive attributes. Meanwhile, we attempt to transform this 5 factors into evaluation carrier to guide the evolution direction of product styling in HIIF-IGA, and then optimized four electric bikes with scores over 8.8 so that it could realize user demand-driven product evolution design. To handle users' ambiguity, the FAHP method is used to quantify the user's emotional imagery criterion and create a product evolution design system platform, which can automatically generate product styling design scheme in line with user preferences. This experimental results show that the proposed method can help enterprises effectively improve customer satisfaction and reduce the cost and time of product development.     

Building respectful interface agents

To provide personalized assistance to users, interface agents have to learn not only a user's preferences and interests with respect to a software application, but also when and how the user prefers to be assisted. Interface agents have to detect the user's intention to determine when to assist the user, and the user's interaction and interruption preferences to provide the right type of assistance without hindering the user's work. In this work we describe a user profiling approach that considers these issues within a user profile and a decision making approach that enables the agent to choose the best type of assistance for a given user in a given situation. We also describe the results obtained when evaluating our proposal in the tourism domain, and we compare these results with some previous ones in the calendar management domain.     

Evaluating the impact of the center control touch screen of new energy vehicles on user visual imagery and preferences

In recent years, with the changes in the international energy situation, new energy vehicles (NEVs) have become an essential choice for people to travel. The center control touch screen (CCTS) of NEVs is an important part of the user's interaction with the vehicle, which is quite different from a conventional fuel vehicle. Therefore, it is necessary to conduct research on the CCTS of NVEs. This study integrates multiple research methods to investigate the visual imagery and user preferences for CCTSs in NEVs. The results of the study show that the horizontal version with small rounded angles without a border is the optimal design solution for the visual imagery dimension, whereas the vertical version with large rounded angles and a border is the worst design solution in terms of the visual imagery dimension. The design without a border can increase users' preference and purchase intention to a certain extent. The results of the above study can effectively reflect the potential user demand for NEVs’ CCTSs, which also has positive implications for enhancing the overall user experience of NEVs.     

MTi: A method for user identification for multitouch displays

This paper describes MTi, a biometric method for user identification on multitouch displays. The method is based on features obtained only from the coordinates of the 5 touchpoints of one of the user's hands. This makes MTi applicable to all multitouch displays large enough to accommodate a human hand and detect 5 or more touchpoints without requiring additional hardware and regardless of the display's underlying sensing technology. MTi only requests that the user places his hand on the display with the fingers comfortably stretched apart. A dataset of 34 users was created on which our method reported 94.69% identification accuracy. The method's scalability was tested on a subset of the Bosphorus hand database (100 users, 94.33% identification accuracy) and a usability study was performed.     

Understanding users’ dynamic behavior in a free trial of IT services: A three-stage model

A free trial of an information technology service (ITS) provides users an opportunity to obtain direct usage experience without purchase commitment, which can significantly reduce their uncertainty about the utility and quality of the ITS and promote their intention of final purchasing. Previous studies of user behavior in a free trial of ITS have mainly focused on either the adoption intention in the pre-trial stage or purchase intention in the post-trial stage. There is a lack of study investigating the trial stage that facilitates the transition and connection between these two separately studied stages. In this study, we view free trial as a motivated learning process and propose a Three-Stage Model (TSM) based on the learning motivation theory and reference-dependent theory to investigate users’ free trial behavior dynamics in moving from motivation to trial, making efforts to gain trial experience, and finally making a further purchase decision after the trial. We collected 377 users’ free trial experience to test our TSM using partial least squares-based structural equation modeling. Our results indicate that the perceived trial benefit and social influence strongly motivate user’s willingness to trial and that the utility experience and flow experience gained through trial effort leads to a willingness to accept and ultimately affect user’s willingness to pay through the mediation effect of expected additional value and price justification. Our study contributes to the theory that explains the dynamics of user decision-making behavior in the context of a free trial of ITS.     

Can eye-tracking data be measured to assess product design?: Visual attention mechanism should be considered

Eye tracking probes user's perception of real-time reaction to products, while conventional methods (i.e. interviews, focus group, questionnaires and so on) have generally failed because they depend on users' willingness and competency to describe how they feel when they are exposed to a product. Two tasks were designed to explore the indexes of eye movement that can reflect user experience of product, and analyse the attention captured by product attributes and goal-oriented. In task one, participants just browsed two smart phone pictures and evaluated the whole user experience. Binary choices were used in task two to ask participants to select the smart phone picture with higher user experience and then click the mouse. The results showed that in the browsing task, participants had shorter time to first fixation for the smart phone picture with higher level of user experience than the lower. And pupil dilated significantly when participants browse smart phone picture with lower level of user experience. In goal-oriented task, participants' attentions were dominated by visual perception of task driven, mainly reflected on longer fixation time and larger pupil diameter when looking at the smart phone with higher level of user experience. These results support the notion that we cannot assess product design just by several eye-movement indexes without considering the effects of visual attention mechanism.Relevance to industryThe appearance of product plays an important role to attract user's attention and stimulate their intention to experience. And vision is the main channel for users to obtain product information. Hence a thorough research on the inherent mechanism of vision perception can provide technical support for product designers, which in turn can attract more consumers to experience the product, even buy it. Moreover, the seller can find out the real buyers and predict their desired products by tracking user's eyes.     

Understanding determinants of information systems users’ behaviour: a comparison of two models in the context of integrated accounting and budgeting software

This study examines determinants that affect the behaviour of information systems (IS) users, and influence of the users’ decisions to continue using IS by two models: the technology acceptance model (TAM) and the expectation-confirmation model of IS continuance (ECM-IS). The behaviour of professionals who utilise complex software solutions for performing their working tasks has been in the focus of this research. Based on data gathered from questionnaires filled-out by users of the integrated accounting and budgeting software (IABS), the confirmatory factor analysis has shown that both models demonstrate good factor, convergence and discriminatory validity, respectively. The comparison of the obtained results has been performed, and it shows that ECM-IS has a larger explanatory power (R²) over TAM, explaining 49% of the dependent variable (IS continuance intention) in relation to 29%. The IS continuance intention is determined by the users’ satisfaction and perceived usefulness. The user satisfaction is influenced by perceived usefulness and confirmation. The confirmation of the user's expectations has had a positive influence on perceived usefulness. The perceived ease of use has not exerted a significant influence on the user's intention of IS continuance.     

An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles

Recommender systems in online shopping automatically select the most appropriate items to each user, thus shortening his/her product searching time in the shops and adapting the selection as his/her particular preferences evolve over time. This adaptation process typically considers that a user's interest in a given type of product always decreases with time from the moment of the last purchase. However, the necessity of a product for a user depends on both the nature of the own item and the personal preferences of the user, being even possible that his/her interest increases over time from the purchase. Some existing approaches focus only on the first factor, missing the point that the influence of time can be very different for different users. To solve this limitation, we present a filtering strategy that exploits the semantics formalized in an ontology in order to link items (and their features) to time functions. The novelty lies within the fact that the shapes of these functions are corrected by temporal curves built from the consumption stereotypes into which each user fits best. Our preliminary experiments involving real users have revealed significant improvements of recommendation precision with regard to previous time-driven filtering approaches.     

Applying event related potentials to evaluate user preferences toward smartphone form design

Product forms can affect user preference and play an important role in user's purchasing decisions. Neuroimaging methods can provide an improved understanding of the mechanisms of decision making, which enhance the ability of enterprises to effectively design their products. Hence event related potentials (ERPs) were applied to explore the brain activity evoked by variety of product forms when users made preference among them. Smartphone product forms were displayed with equiprobability randomly. Participants were asked to click the left mouse button when they preferred one product form, else the right button for nonpreferred. The brain signals of each participant were recorded by Curry 7.0. Finally, brain signals were processed by using Curry 7.0 SBA and SPSS 18.0 software. The results showed that preferred product forms evoked enhanced N2, P2 and P3. Moreover, there were significant correlation between ERPs and behavioural data, participants devoted more attention and had faster responding time to preferred products than to nonpreferred. These results indicate that the differences of ERPs can be used to evaluate user preference.Relevance to industryThe integration of customer preferences is nowadays a challenge in new product development. Hence a thorough research on the inherent mechanism of preference formation can provide an accurate measurement method of user's perception. The differences of brain signals evoked by product forms can also provide technical support for product designers, which in turn can meet with user experience. Moreover, the results can be taken as evaluating indicators of product design.     

The influence of user expertise and prototype fidelity in usability tests

An empirical study examined the impact of user expertise and prototype fidelity on the outcomes of a usability test. User expertise (expert vs. novice) and prototype fidelity (paper prototype, 3D mock-up, and fully operational appliance) were manipulated as independent variables in a 2 × 3 between-subjects design. Employing a floor scrubber as a model product, 48 users carried out several cleaning tasks. Usability problems identified by participants were recorded. Furthermore, performance, system management strategies and perceived usability were measured. The results showed that experts reported more usability problems than novices but these were considered to be less severe than those reported by novices. Reduced fidelity prototypes were generally suitable to predict product usability of the real appliance. The implications for the running of usability tests are specific to the fidelity of the prototype.     

基于知识图谱的用户表征及在互补产品推荐中的应用

互补产品推荐旨在为用户提供经常一起购买的产品,以满足共同的需求。现有的互补产品推荐方法大多考虑对产品的内容特性(视觉和文本内容)建模,而没有考虑用户购买产品的偏好。为此设计了一种融合用户偏好的互补产品推荐模型(complementary product recommendation models that integrate user preferences, CPRUP)。该模型首先计算产品之间图像和文本特征的互补关系;然后将知识图谱与注意力机制相结合,基于n-hop邻居挖掘用户历史购买产品之间的相关性,提出一种基于知识图谱的用户表征来提取用户对互补产品的偏好;最后基于神经网络实现互补关系与用户偏好的共同学习。使用Amazon数据集进行实验,提出的CPRUP模型与次优基线模型相比,ACC提升了5%,precision提升了4%,表明CPRUP模型可以更准确地为用户推荐互补产品。     

Application of covering rough granular computing model in collaborative filtering recommendation algorithm optimization

Data sparseness will reduce the accuracy and diversity of collaborative filtering recommendation algorithms. In response to this problem, using granular computing model to realize the nearest neighbor clustering, and a covering rough granular computing model for collaborative filtering recommendation algorithm optimization is proposed. First of all, our method is built on the historical record of the user's rating of the item, the user’s predilection threshold is set under the item type layer to find the user's local rough granular set to avoid data sparsity. Then it combines the similarity between users. Configuring the covering coefficient for target user layer, it obtained the global covering rough granular set of the target user. So it solved the local optimal problem caused by data sparsity. Completed the coarse–fine-grained conversion in the covering rough granular space, obtain a rough granular computing model with multiple granular covering of target users, it improved the diversity of the recommendation system. All in all, predict the target users’ score and have the recommendation. Compared experiments with six classic algorithms on the public MovieLens data set, the results showed that the optimized algorithm not only has enhanced robustness under the premise of equivalent time complexity, but also has significantly higher recommendation diversity as well as accuracy.     

The influence of user cognition on consumption decision-making from the perspective of bounded rationality

Consumer decision-making is related to the success or failure of enterprises, and products that cater to the cognitive preferences of users have become a focus of current research. Based on the theory of bounded rationality, this paper explores the cognitive process of consumer decision-making. Then, how product shape affects consumption decision-making is analyzed with eye-tracking technology. Finally, the design principle of the product form is further explored. The results demonstrate the following: (1) the perceptual cognition of users has a driving effect on consumption behavior; (2) as a key factor affecting the perceptual cognition of users, product form affects consumer decision-making by influencing the degree of approach motivation; (3) by establishing the mapping relationship between product form elements and user images, the principles of product form design can be more consistent with user image preferences. This study provides useful suggestions for how to increase the purchase behavior of users from the perspective of bounded rationality.     

基于注意力机制的规范化矩阵分解推荐算法

近年来,矩阵分解(MF)技术因其有效性和简便性在推荐系统中得到广泛应用.但是,数据稀疏和冷启动问题导致MF学习到的用户特征向量不能准确地代表用户的偏好以及反映用户间的相似关系,影响了模型的性能.为了解决该问题,规范化矩阵分解(RMF)技术引起了研究者的关注.挖掘用户间可靠的相似关系,是RMF需要解决的问题.此外,MF将目标用户特征向量和目标项目特征向量的内积作为目标用户对目标项目的评分,这种简单的线性关系忽略了用户对项目各个属性特征不同的关注度.如何分析用户对项目属性特征的关注度,获取用户更准确的偏好,仍然是一个挑战.针对上述问题,提出了基于注意力机制的规范化矩阵分解模型(ARMF).具体地,为了获取用户间可靠的相似关系解决数据稀疏和冷启动问题,该模型同时依据用户信任网络和评分记录构建用户-项目异构网络,并基于该异构网络挖掘用户间的相似关系;为了进一步提升模型性能,通过在MF中引入注意力机制,分析用户对项目各个属性特征不同的关注度来获取用户更准确的偏好.最后,在两个真实数据集上对比ARMF与现有工作,实验结果证明,ARMF有更好的准确性和健壮性.     

Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

The motivation of collaborative filtering (CF) comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinion-rich resources such as online reviews, new opportunities arise as we can identify the preferences from user opinions. The main idea of our approach is to elicit user opinions from online reviews, and map such opinions into preferences that can be understood by CF-based recommender systems. We divide recommender systems into two types depending on the number of product category recommended: the multiple-category recommendation and the single-category recommendation. For the former, sentiment polarity in coarse-grained manner is identified while for the latter fine-grained sentiment analysis is conducted for each product aspect. If the evaluation frequency for an aspect by a user is greater than the average frequency by all users, it indicates that the user is more concerned with that aspect. If a user's rating for an aspect is lower than the average rating by all users, he or she is much pickier than others on that aspect. Through sentiment analysis, we then build an opinion-enhanced user preference model, where the higher the similarity between user opinions the more consistent preferences between users are. Experiment results show that the proposed CF algorithm outperforms baseline methods for product recommendation in terms of accuracy and recall.     

基于社交信任和标签偏好的景点推荐方法

针对现有的景点推荐算法在处理用户关系时忽视了用户隐性信任和信任传递问题,以及当用户处于新城市时由于缺乏用户历史记录无法做出准确推荐的情况,本文提出一种综合用户信任关系和标签偏好的个性化景点推荐方法.在仅仅考虑用户相似度时推荐质量差的情况下引入信任度,通过挖掘用户隐性信任关系解决了现有研究在直接信任难以获取时无法做出推荐的情况,有效缓解了数据稀疏性和冷启动问题.同时在用户兴趣分析过程中将景点和标签的关系扩展到了用户、景点和标签三者的相互关系,把用户的兴趣偏好分解成对不同景点标签的长期偏好,有效地缓解了缺乏用户历史游览记录时推荐质量不佳的问题.通过在Flickr网站上收集的数据进行实验验证,结果表明本文提出的混合推荐算法有效地提高了推荐精度,在一定程度上缓解了冷启动和新城市问题.     

An adaptive website system to improve efficiency with web mining techniques

The paper proposes an adaptive web system—that is, a website that is capable of changing its original design to fit user requirements. For the purpose of improving shortcomings of the website, and also to make it much easier for users to access information, the system analyzes user browsing patterns from their access records. This paper concentrates on the operating-efficiency of a website—that is, the efficiency with which a group of users browse a website. By achieving high efficiency, users spend less operating cost to accomplish a desired user goal. Based on user access data, we analyze each user's operating activities as well as their browsing sequences. With this data, we can calculate a measure of the efficiency of the user's browsing sequences. The paper develops an algorithm to accurately calculate this efficiency and to suggest how to increase the efficiency of user operations. This can be achieved in two ways: (i) by adding a new link between two web pages, or (ii) by suggesting to designers to reconsider existing inefficient links so as to allow users to arrive at their target pages more quickly. Using this algorithm, we develop a prototype to prove the concept of efficiency. The implementation is an adaptive website system to automatically change the website architecture according to user browsing activities and to improve website usability from the viewpoint of efficiency.     

Prognostic normative reasoning

Human users planning for multiple objectives in complex environments are subjected to high levels of cognitive workload, which can severely impair the quality of the plans created. This paper describes a software agent that can proactively assist cognitively overloaded users by providing normative reasoning about prohibitions and obligations so that the user can focus on her primary objectives. In order to provide proactive assistance, we develop the notion of prognostic normative reasoning (PNR) that consists of the following steps: (1) recognizing the user's planned activities, (2) reasoning about norms to evaluate those predicted activities, and (3) providing necessary assistance so that the user's activities are consistent with norms. The idea of PNR integrates various AI techniques, namely, user intention recognition, normative reasoning over a user's intention, and planning, execution and replanning for assistive actions. In this paper, we describe an agent architecture for PNR and discuss practical applications.     

Incremental critiquing

Conversational recommender systems guide users through a product space, alternatively making concrete product suggestions and eliciting the user's feedback. Critiquing is a common form of user feedback, where users provide limited feedback at the feature-level by constraining a feature's value-space. For example, a user may request a cheaper product, thus critiquing the price feature. Usually, when critiquing is used in conversational recommender systems, there is little or no attempt to monitor successive critiques within a given recommendation session. In our experience this can lead to inefficiencies on the part of the recommender system, and confusion on the part of the user. In this paper we describe an approach to critiquing that attempts to consider a user's critiquing history, as well as their current critique, when making new recommendations. We provide experimental evidence to show that this has the potential to significantly improve recommendation efficiency.