Extending the engineering trade-off analysis by integrating user preferences in conjoint analysis

The ongoing technical improvements in architecture design with improved features of mobile or smartphones do not automatically guarantee user acceptance, because technical and commercial aspects primarily drive the development of mobile communication systems and devices. Especially in early stages of technology development, user preferences and values are not adequately considered, which might even have a negative impact on acceptance issues. The aim of this study was the implementation of a quantified understanding of user needs in terms of values into the system design process of cell-phone processors. Moreover, we aimed for an extension of the engineering’s trade-off analysis by using conjoint analysis in order to investigate trade-offs between specific device characteristics. Finally, our aim was the evaluation of empirically based user-oriented research methods.Results of the first study revealed that battery life, speech quality, signal quality and data-transmission rate are the most important device characteristics. Results from conjoint analysis indicated a clear trade-off between battery life and the three other characteristics. Moreover, this research demonstrated that technology acceptance research benefits considerably from an interdisciplinary and multi-method approach. Besides, implementing the users’ preferences into early stages of the product development process offers several advantages concerning effectiveness as well as economic aspects of development.     

Predicting user preferences of environment design: a perceptual mechanism of user interface customisation

It is a well-known fact that users vary in their preferences and needs. Therefore, it is very crucial to provide the customisation or personalisation for users in certain usage conditions that are more associated with their preferences. With the current limitation in adopting perceptual processing into user interface personalisation, we introduced the possibility of inferring interface design preferences from the user’s eye-movement behaviour. We firstly captured the user’s preferences of graphic design elements using an eye-tracker. Then we diagnosed these preferences towards the region of interests to build a prediction model for interface customisation. The prediction models from eye-movement behaviour showed a high potential for predicting users’ preferences of interface design based on the paralleled relation between their fixation and saccadic movement. This mechanism provides a novel way of user interface design customisation and opens the door for new research in the areas of human–computer interaction and decision-making.     

Analyzing social choice and group ranking of online games for product mix innovation

With the increasing populations of the smartphones and mobile devices these years, the majority of people have played the mobile or online games on their smartphones. As such users have rapidly increased, this research is to propose an innovative service that can automatically recommend the users the appropriate online games as their optional references. In order for market competition, the most online games’ enterprises need to develop and sell the newer online game products or versions continuously for the customers who like to play online games. Now that the types and markets of online games have had the more diverse along with the sustainable development of online game products over time. This research is to propose an integrative analysis from a large amount of data -users’ preference sequences. Firstly, the research utilizes a sequence recommendation technology that can be used to analyze their preference sequences of online game’s types based on other similar users’ sequence preference data. In other words, these recommended online game’s types that can be generated according to the relationships between the query user’s preferences and users themselves. All sequences from numerous data are accumulated to be the inferences of the query users’ preferences. Secondly, the research also utilizes data mining methodology to explore the possibilities of different product mix of game types from numerous user data. For the main methodology, this research uses a ‘partial user ranking algorithm’ to analyze group ranking of online games based on similar users. The purpose is to explore how the players can be recommended based on the other users’ priorities, as well as the product mix of game types which can be the potential business trends come to markets. Furthermore, this study contributes to a research implication for online game industry, and the results also could be the business references of game combinations and product design.     

Joint user knowledge and matrix factorization for recommender systems

Currently, most of the existing recommendation methods treat social network users equally, which assume that the effect of recommendation on a user is decided by the user’s own preferences and social influence. However, a user’s own knowledge in a field has not been considered. In other words, to what extent does a user accept recommendations in social networks need to consider the user’s own knowledge or expertise in the field. In this paper, we propose a novel matrix factorization recommendation algorithm based on integrating social network information such as trust relationships, rating information of users and users’ own knowledge. Specifically, since we cannot directly measure a user’s knowledge in the field, we first use a user’s status in a social network to indicate a user’s knowledge in a field, and users’ status is inferred from the distributions of users’ ratings and followers across fields or the structure of domain-specific social network. Then, we model the final rating of decision-making as a linear combination of the user’s own preferences, social influence and user’s own knowledge. Experimental results on real world data sets show that our proposed approach generally outperforms the state-of-the-art recommendation algorithms that do not consider the knowledge level difference between the users.     

The effect of visual feedback types on the wait indicator interface of a mobile application

This study examines the essential effects of visual feedback on users’ perceptions of time and on their evaluations of waiting time for a mobile application user interface. The two variables adopted in this experiment are wait indicator visual feedback and waiting time. Three types of visual feedback were used: bar indicator, pie indicator, and cartoon indicator. Three waiting times were used: 2 s, 5 s, and 10 s. This study was conducted based on the attentional-gate model of Zakay and Block to help investigate user time perceptions. The experiment of this study was a 3 × 3 mixed factorial design to measure the impact of the research variables on the users’ perceptions of waiting time, wait evaluations, and preferences. Results showed that the users’ time perceptions and wait evaluations were affected by the feedback types and waiting times, and the results are consistent with Vierordt's law and the attentional-gate model. Furthermore, there was a positive impact on the participants' preferences when they perceived a shorter waiting time. The cartoon type feedback can help reduce the perception of waiting time, but it does not necessarily improve user experience regarding short waiting time.     

On optimal preference diffusion over social networks

It was well observed that a user’s preference over a product changes based on his/her friends’ preferences, and this phenomenon is called “preference diffusion”, and several models have been proposed for modeling the preference diffusion process. These models share an idea that the diffusion process involves many iterations, and in each iteration, each user has his/her preference affected by some other preferences (e.g., those of his/her friends). When computing users’ preferences after a certain number of iterations, these models use users’ preferences at the end of that iteration only, which we believe is not desirable since users’ preferences at the end of other iterations should also have some effects on users’ final preferences. Therefore, in this paper, we propose a new model for preference diffusion, which takes into consideration users’ preferences at each iteration for computing users’ final preferences. Under the new model, we study two problems for optimizing the preference diffusion process with respect to two different objectives. One is easy to solve for which we design an exact algorithm and the other is NP-hard for which we design a $(1-1∕e)$-factor approximate algorithm. We conducted extensive experiments on real datasets which verified our proposed model and algorithms.     

一种基于用户播放行为序列的个性化视频推荐策略

本文针对在线视频服务网站的个性化推荐问题,提出了一种基于用户播放行为序列的个性化推荐策略.该策略通过深度神经网络词向量模型分析用户播放视频行为数据,将视频映射成等维度的特征向量,提取视频的语义特征.聚类用户播放历史视频的特征向量,建模用户兴趣分布矩阵.结合用户兴趣偏好和用户观看历史序列生成推荐列表.在大规模的视频服务系统中进行了离线实验,相比随机算法、基于物品的协同过滤和基于用户的协同过滤传统推荐策略,本方法在用户观看视频的Top-N推荐精确率方面平均分别获得22.3%、30.7%和934%的相对提升,在召回率指标上分别获得52.8%、41%和1065%的相对提升.进一步地与矩阵分解算法SVD++、基于双向LSTM模型和注意力机制的Bi-LSTM+Attention算法和基于用户行为序列的深度兴趣网络DIN比较,Top-N推荐精确率和召回率也得到了明显提升.该推荐策略不仅获得了较高的精确率和召回率,还尝试解决传统推荐面临大规模工业数据集时的数据要求严苛、数据稀疏和数据噪声等问题.     

Beyond the User Preferences: Aligning the Prototype Design to the Users’ Expectations

It is important for practitioners to conceptualize and tailor a prototype in tune with the users’ expectations in the early stages of the design life cycle so the modifications of the product design in advanced phases are kept to a minimum. According to user preference studies, the aesthetic and the usability of a system play an important role in the user appraisal and selection of a product. However, user preferences are just a part of the equation. The fact that a user prefers one product over the other does not mean that he or she would necessarily buy it. To understand the factors affecting the user's assessment of a product before the actual use of the product and the user's intention to purchase the product we conducted a study, reported in this article. Our study, a modification of a well‐known protocol, considers the users’ preferences of six simulated smartphones each with different combination of attributes. A sample consisting of 365 participants was involved in our analysis. Our results confirm that the main basis for the users’ pre‐use preferences is the aesthetics of the product, whereas our results suggest that the main basis for the user's intention to purchase are the expected usability of the product. Moreover, our analysis reveals that the personal characteristics of the users have different effects on both the users’ preferences and their intention to purchase a product. These results suggest that the designers should carefully balance the aesthetics and usability features of a prototype in tune with the users expectations. If the conceptualization of a product is done properly the redesign cycles after the usability testing can be reduced and speed up the process for releasing the product on the market.     

Tuning user profiles based on analyzing dynamic preference in document retrieval systems

Modeling users’ preferences and needs is one of the most important personalization tasks in information retrieval domain. In this paper a model for user profile tuning in document retrieval systems is considered. Methods for tuning the user profile based on analysis of user preferences dynamics are experimentally evaluated to check whether with growing history of user activity the created user profile can converge to his preferences. As the statistical analysis of series of simulations has shown, proposed methods of user profile actualization are effective in the sense that the distance between user preferences and his profile becomes smaller and smaller along with time.     

Dynamic adaptation of numerical attributes in a user profile

Recommender systems try to help users in their decisions by analyzing and ranking the available alternatives according to their preferences and interests, modeled in user profiles. The discovery and dynamic update of the users’ preferences are key issues in the development of these systems. In this work we propose to use the information provided by a user during his/her interaction with a recommender system to infer his/her preferences over the criteria used to define the decision alternatives. More specifically, this paper pays special attention on how to learn the user’s preferred value in the case of numerical attributes. A methodology to adapt the user profile in a dynamic and automatic way is presented. The adaptations in the profile are performed after each interaction of the user with the system and/or after the system has gathered enough information from several user selections. We have developed a framework for the automatic evaluation of the performance of the adaptation algorithm that permits to analyze the influence of different parameters. The obtained results show that the adaptation algorithm is able to learn a very accurate model of the user preferences after a certain amount of interactions with him/her, even if the preferences change dynamically over time.     

中国机器翻译研究的机遇与挑战——第八届全国机器翻译研讨会总结与展望

随着统计方法逐渐成为机器翻译研究的主流,机器翻译系统评测的分值越来越高,人们对机器翻译的信心和期望逐渐增加,社会对机器翻译应用的需求也越来越大。然而,现有的机器翻译理论和方法在系统性能上提升的空间逐渐减小,而且距离用户实际需求仍有很长的路要走。那么,面对期望、面对需求,机器翻译之路应该如何走？为此,第八届全国机器翻译研讨会对当前机器翻译研究所面临的挑战和机遇进行了深入研讨。该文详细介绍了该次研讨会六个专题的讨论情况,对机器翻译研究面临的机遇和挑战进行了认真的分析和总结。     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.     

User preferences for adaptive user interfaces in health information systems

An adaptive user interface requires identification of user requirements. Interface designers and engineers must understand end-user interaction with the system to improve user interface design. A combination of interviews and observations is applied for user requirement analysis in health information systems (HIS). Then, user preferences are categorized in this paper as either data entry, language and vocabulary, information presentation, or help, warning and feedback. The user preferences in these categories were evaluated using the focus group method. Focus group sessions with different types of HIS users comprising medical staff (with and without computer skills) and system administrators identified each user group’s preference for the initial adaptation of the HIS user interface. User needs and requirements must be identified to adapt the interface to users during data entry into the system. System designers must understand user interactions with the system to identify their needs and preferences. Without this, interface design cannot be adapted to users and users will not be comfortable using the system and eventually abandon its use.     

User Personality and User Satisfaction with Recommender Systems

In this study, we show that individual users’ preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate that we can extract strong signals about individual preferences for recommendation diversity, popularity and serendipity by measuring their personality traits. We conducted an online experiment with over 1,800 users for six months on a live recommendation system. In this experiment, we asked users to evaluate a list of movie recommendations with different levels of diversity, popularity, and serendipity. Then, we assessed users’ personality traits using the Ten-item Personality Inventory (TIPI). We found that ratings-based recommender systems may often fail to deliver preferred levels of diversity, popularity, and serendipity for their users (e.g. users with high-serendipity preferences). We also found that users with different personalities have different preferences for these three recommendation properties. Our work suggests that we can improve user satisfaction when we integrate users’ personality traits into the process of generating recommendations.     

结合动态多类信息的兴趣点推荐

利用用户的历史签到数据的同时考虑用户的长期偏好和短期偏好已成为当今兴趣点（Point-of-Interest, POI）推荐的主流方法之一。然而,现有方法往往忽略了用户评论中隐含的用户偏好信息,忽视了不同用户在对长期偏好和短期偏好的依赖上所存在的差异性。针对上述局限,本文提出一种结合动态多类信息的兴趣点推荐方法DMGCR。首先,利用注意力机制捕获用户对不同兴趣点的关注程度,定量刻画用户对兴趣点的长期偏好。其次,将评论信息与已有的位置和类别信息相结合,并利用双向长短期记忆网络学习评论文本中隐含的语义特征,在捕获用户对兴趣点情感倾向的基础上准确刻画用户的短期偏好。最后,设计融合动态多类信息的用户偏好综合预测函数,实现下一个兴趣点推荐概率的定量计算。多个数据集上的实验结果验证了该方法在推荐性能上的有效性和优越性。     

Produktempfehlungssysteme mit minimalem Konsumentenaufwand und hoher Genauigkeit

In recent studies on recommendation systems, the choice-based conjoint analysis has been suggested as a method for measuring consumer preferences. This approach achieves high recommendation accuracy and does not suffer from the start-up problem because it is also applicable for recommendations for new consumers or of new products. However, this method requires massive consumer input, which causes consumer reluctance. In a simulation study, we demonstrate the high accuracy, but also the high user’s effort for using a utility-based recommendation system using a choice-based conjoint analysis with hierarchical Bayes estimation. In order to reduce the conflict between consumer effort and recommendation accuracy, we develop a novel approach that only shows Pareto-efficient alternatives and ranks them according to the number of dominated attributes. We demonstrate that, in terms of the decision accuracy of the recommended products, the ranked Pareto-front approach performs better than a recommendation system that employs choice-based conjoint analysis. Furthermore, the consumer’s effort is kept low and comparable to that of simple systems that require little consumer input.     

基于卷积神经网络交互的用户属性偏好建模的推荐模型

潜在因子模型（LFM）以其优异的性能在推荐领域得到了广泛应用。在LFM中除了使用交互数据以外,辅助信息也被引入用于解决数据稀疏的问题,从而提升推荐的性能。然而,大多数LFM仍然存在一些问题：第一,LFM在对用户进行建模时,忽略了用户如何根据其特征偏好对项目作出决策;第二,采用内积的特征交互假设特征维度之间是相互独立的,而没有考虑到特征维度之间的关联。针对上述问题,提出一种新的推荐模型：基于卷积神经网络（CNN）交互的用户属性偏好建模的推荐模型（UAMC）。该模型首先获得用户的一般偏好、用户属性和项目嵌入,然后将用户属性和项目嵌入进行交互,以探索用户不同的属性对不同项目的偏好;接着将交互过的用户偏好属性送入CNN层来探索不同偏好属性的不同维度的关联,从而得到用户的属性偏好向量;接着使用注意力机制结合用户的一般偏好和CNN层得到的属性偏好,从而获得用户的向量表示;最后采用点积来计算用户对项目的评分。在Movielens-100K、Movielens-1M和Book-crossing这三个真实的数据集上进行了实验。实验结果表明,所提模型在均方根误差（RMSE）上与稀疏数据预测的神经网络分解机（NFM）模型相比分别降低了1.75%、2.78%和0.25%,验证了在LFM的评分预测推荐中,UAMC在提升推荐精度上的有效性。     

RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment

With an ever-increasing accessibility to different multimedia contents in real-time, it is difficult for users to identify the proper resources from such a vast number of choices. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is a need to reinforce the recommendation process in a systematic way, with context-adaptive information. The contributions of this paper are twofold. First, we propose a framework, called RecAm, which enables the collection of contextual information and the delivery of resulted recommendation by adapting the user’s environment using Ambient Intelligent (AmI) Interfaces. Second, we propose a recommendation model that establishes a bridge between the multimedia resources, user joint preferences, and the detected contextual information. Hence, we obtain a comprehensive view of the user’s context, as well as provide a personalized environment to deliver the feedback. We demonstrate the feasibility of RecAm with two prototypes applications that use contextual information for recommendations. The offline experiment conducted shows the improvement of delivering personalized recommendations based on the user’s context on two real-world datasets.     

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.