融合用户社会地位和矩阵分解的推荐算法

随着社交网络服务的日益流行,社交网络平台为推荐算法提供了丰富的额外信息.假设朋友之间共享更多的共同偏好并且用户往往易于接受来自朋友的推荐,越来越多的推荐系统利用社交网络中用户之间的信任关系来改进传统推荐算法的性能.然而,现有基于社交网络推荐算法忽略了2个问题：1)在不同的领域中,用户信任不同的朋友;2)由于用户在不同的领域内具有不同的社会地位,因此,用户在不同的领域内受朋友的影响程度是不同的.首先利用整体的社交网络结构信息和用户的评分信息推导特定领域社交网络结构,然后利用PageRank算法计算用户在特定领域的社会地位,最后提出了一种融合用户社会地位信息的矩阵分解推荐算法.在真实数据集上的实验结果表明：融合用户地位信息的矩阵分解推荐算法的性能优于传统的基于社交网络推荐算法.     

SmallWorlds: Visualizing Social Recommendations

We present SmallWorlds, a visual interactive graph‐based interface that allows users to specify, refine and build item‐preference profiles in a variety of domains. The interface facilitates expressions of taste through simple graph interactions and these preferences are used to compute personalized, fully transparent item recommendations for a target user. Predictions are based on a collaborative analysis of preference data from a user's direct peer group on a social network. We find that in addition to receiving transparent and accurate item recommendations, users also learn a wealth of information about the preferences of their peers through interaction with our visualization. Such information is not easily discoverable in traditional text based interfaces. A detailed analysis of our design choices for visual layout, interaction and prediction techniques is presented. Our evaluations discuss results from a user study in which SmallWorlds was deployed as an interactive recommender system on Facebook.     

基于强化学习DQN的智能体信任增强

信任推荐系统是以社交网络为基础的一种重要推荐系统应用,其结合用户之间的信任关系对用户进行项目推荐.但之前的研究一般假定用户之间的信任值固定,无法对用户信任及偏好的动态变化做出及时响应,进而影响推荐效果.实际上,用户接受推荐后,当实际评价高于心理预期时,体验用户对推荐者的信任将增加,反之则下降.针对此问题,并且重点考虑用户间信任变化过程及信任的动态性,提出了一种结合强化学习的用户信任增强方法.因此,使用最小均方误差算法研究评价差值对用户信任的动态影响,利用强化学习方法deep q-learning(DQN)模拟推荐者在推荐过程中学习用户偏好进而提升信任值的过程,并且提出了一个多项式级别的算法来计算信任值和推荐,可激励推荐者学习用户的偏好,并使用户对推荐者的信任始终保持在较高程度.实验表明,方法可快速响应用户偏好的动态变化,当其应用于推荐系统时,相较于其他方法,可为用户提供更及时、更准确的推荐结果.     

A novel recommendation approach based on users’ weighted trust relations and the rating similarities

With the growing popularity of open social networks, approaches incorporating social relationships into recommender systems are gaining momentum, especially matrix factorization-based ones. The experiments in previous literatures indicate that social information is very effective in improving the performance of traditional recommendation algorithms. However, most of existing social recommendation methods only take one kind of social relations—trust information into consideration, which is far from satisfactory. Furthermore, most of the existing trust networks are binary, which results in the equal treatment to different users who are trusted by the same user in these methods. In this paper, based on matrix factorization methods, we propose a new approach to make recommendation with social information. Its novelty can be summarized as follows: (1) it shows how to add different weights on the social trust relationships among users based on the trustee’s competence and trustworthiness; (2) it incorporates the similarity relationships among users as a complement into the social trust relationships to enhance the computation of user’s neighborhood; (3) it can balance the influence of these two kinds of relationships based on user’s individuality adaptively. Experiments on Epinions and Ciao datasets demonstrate that our approach outperforms the state-of-the-art algorithms in terms of mean absolute error and root mean square error, in particular for the users who rated a few items.     

Blockchain-based recommender systems: Applications,challenges and future opportunities

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models’ accuracy and ignore issues related to security and the users’ privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users’ private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research.     

Experiments on user experiences with recommender interfaces

Recommender systems have been increasingly adopted as personalisation services in e-commerce. They facilitate users to locate items which they would be interested in viewing or purchasing. However, most studies have emphasised on the algorithm's performance, rather than on in-depth analysis of user experiences with the recommender interface. In this article, we report the results of two studies that compared two recommender interfaces: the organisation-based interface (where recommendations are presented in a category structure via the preference-based organisation method) and the standard ranked list (where recommendations are listed one after the other as ordered by their prediction scores).The first study focuses on evaluating users' eye-movement behaviour in these interfaces. With the help of an eye tracker, we found that the organisation interface (ORG) can significantly attract users' attentions to more recommended items. As a result, more users made product choices in that interface. The second, larger-scale, cross-cultural user survey further shows that the ORG performed significantly better in terms of enhancing users' perceived recommendation quality, perceived ease of use and perceived usefulness of the system. Hence, these empirical findings suggest that the change of recommender interface design can not only alter users' attention distribution, but also influence their subjective attitudes towards the system.     

Sequence-based trust in collaborative filtering for document recommendation

Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust values based on users' past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, conventional trust-based CF methods do not address the issue of deriving the trust values based on users' various information needs on items over time. In knowledge-intensive environments, users usually have various information needs in accessing required documents over time, which forms a sequence of documents ordered according to their access time. We propose a sequence-based trust model to derive the trust values based on users' sequences of ratings on documents. The model considers two factors – time factor and document similarity – in computing the trustworthiness of users. The proposed model enhanced with the similarity of user profiles is incorporated into a standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method in comparison with other trust-based recommender systems.     

Personalized detection of user intentions

Interface agents are strategic software components for improving the quality of services to users. In order to be accepted by users, interface agents need to make useful suggestions always in the context of the user’s intention. The user’s intention should be detected as soon as possible so that the agent can define a way to collaborate with the user. Plan recognition can be applied to identify the user’s goal based on his or her actions in the environment. However, classical approaches to plan recognition fail in two main aspects that make them unsuitable for being used by interface agents: the lack of personalization and the lack of consideration of the transition between different goals pursued by the user. We propose an approach to capture intentions taking into account the variables involved in the application domain that represent the user preferences. Experimental evaluations show us that we have found a way for early detection of intentions.     

On the role of trust in collaborative Web search

Recommender systems combine ideas from information retrieval, user modelling, and artificial intelligence to focus on the provision of more intelligent and proactive information services. As such, recommender systems play an important role when it comes to assisting the user during both routine and specialised information retrieval tasks. Like any good assistant it is important that users can trust in the ability of a recommender system to respond with timely and relevant suggestions. In this paper, we will look at a collaborative recommendation system operating in the domain of Web search. We will show how explicit models of trust can help to inform more reliable recommendations that translate into more relevant search results. Moreover, we demonstrate how the availability of this trust-model facilitates important interface enhancements that provide a means to declare the provenance of result recommendations in a way that will allow searchers to evaluate their likely relevance based on the reputation and trustworthiness of the recommendation partners behind these suggestions.     

Semantic inference of user’s reputation and expertise to improve collaborative recommendations

Collaborative recommender systems select potentially interesting items for each user based on the preferences of like-minded individuals. Particularly, e-commerce has become a major domain in these research field due to its business interest, since identifying the products the users may like or find useful can boost consumption. During the last years, a great number of works in the literature have focused in the improvement of these tools. Expertise, trust and reputation models are incorporated in collaborative recommender systems to increase their accuracy and reliability. However, current approaches require extra data from the users that is not often available. In this paper, we present two contributions that apply a semantic approach to improve recommendation results transparently to the users. On the one hand, we automatically build implicit trust networks in order to incorporate trust and reputation in the selection of the set of like-minded users that will drive the recommendation. On the other hand, we propose a measure of practical expertise by exploiting the data available in any e-commerce recommender system – the consumption histories of the users.     

融合用户信任模型的协同过滤推荐算法

协同过滤推荐是电子商务系统中最为重要的技术之一.随着电子商务系统中用户数目和商品数目的增加,用户-项目评分数据稀疏性问题日益显著.传统的相似度度量方法是基于用户共同评分项目计算的,而过于稀疏的评分使得不能准确预测用户偏好,导致推荐质量急剧下降.针对上述问题,本文考虑用户评分相似性和用户之间信任关系对推荐结果的影响,利用层次分析法实现用户信任模型的构建,提出一种融合用户信任模型的协同过滤推荐算法.实验结果表明： 该算法能够有效反映用户认知变化,缓解评分数据稀疏性对协同过滤推荐算法的影响,提高推荐结果的准确度.     

The Effects of Hover Interface on Users’ Behavioral Multitasking Intention

As the number of features with smartphones is increasing, user interfaces have been advanced considerably with innovative ways for providing useful, multitasking interfaces. In particular, the z-axis of the user interface has been considered to help multitaskers control their smartphone more easily. However, relatively little research has been conducted on the significance of the z-axis on task switching. The main research goal of this study is to explain how a z-axis can affect the experience of multitasking on a mobile device. In particular, the authors focused on a hover interface because of its high functional relevance to multitasking as a form of task switching. Theoretically, the authors provide a conceptual model based on the concept of spatial presence. Systemically, they suggest the important system factors for implementing z-axis interface technology: controllability and naturalness. With regards to users, the authors assume that if the experience with the z-axis interface is increased, users’ intention to multitask is also increased, even when the task complexity is high. The influence of controllability on perceived spatial presence by the hover interface was negatively validated, and the influence of naturalness was positively validated. In addition, the influence of perceived spatial presence on users’ behavioral multitasking intention was positively validated. Finally, the influence from perceived spatial presence to users’ behavioral multitasking intention in the high level of task complexity was significantly stronger than in the low level. Implications and limitations of the study results are discussed in the final section of the article.     

Designing for augmented attention: Towards a framework for attentive user interfaces

Attentive user interfaces are user interfaces that aim to support the user’s attentional capacities. By sensing the users’ attention for objects and people in their everyday environment, and by treating user attention as a limited resource, these interfaces avoid today’s ubiquitous patterns of interruption. Focusing upon attention as a central interaction channel allows development of more sociable methods of communication and repair with ubiquitous devices. Our methods are analogous to human turn taking in group communication. Turn taking improves the user’s ability to conduct foreground processing of conversations. Attentive user interfaces bridge the gap between the foreground and periphery of user activity in a similar fashion, allowing users to move smoothly in between.We present a framework for augmenting user attention through attentive user interfaces. We propose five key properties of attentive systems: (i) to sense attention; (ii) to reason about attention; (iii) to regulate interactions; (iv) to communicate attention and (v) to augment attention.     

Exploiting two-faceted web of trust for enhanced-quality recommendations

Traditional collaborative filtering (CF) based recommender systems on the basis of user similarity often suffer from low accuracy because of the difficulty in finding similar users. Incorporating trust network into CF-based recommender system is an attractive approach to resolve the neighbor selection problem. Most existing trust-based CF methods assume that underlying relationships (whether inferred or pre-existing) can be described and reasoned in a web of trust. However, in online sharing communities or e-commerce sites, a web of trust is not always available and is typically sparse. The limited and sparse web of trust strongly affects the quality of recommendation. In this paper, we propose a novel method that establishes and exploits a two-faceted web of trust on the basis of users’ personal activities and relationship networks in online sharing communities or e-commerce sites, to provide enhanced-quality recommendations. The developed web of trust consists of interest similarity graphs and directed trust graphs and mitigates the sparsity of web of trust. Moreover, the proposed method captures the temporal nature of trust and interest by dynamically updating the two-faceted web of trust. Furthermore, this method adapts to the differences in user rating scales by using a modified Resnick’s prediction formula. As enabled by the Pareto principle and graph theory, new users highly benefit from the aggregated global interest similarity (popularity) in interest similarity graph and the global trust (reputation) in the directed trust graph. The experiments on two datasets with different sparsity levels (i.e., Jester and MovieLens datasets) show that the proposed approach can significantly improve the predictive accuracy and decision-support accuracy of the trust-based CF recommender system.     

Incorporating trust-in-technology into Expectation Disconfirmation Theory

Continued use of strategic information systems is not always a given. This study proposes that users’ trust in the system may influence their satisfaction and continuance intention. While trust has been found to have strategic implications for understanding consumers’ technology usage, relatively little research has examined how trust’s influence operates over time. To gain insight into trust’s influence on strategic system usage over time and to explain how trust relates to satisfaction and continuance intention, we integrate trust-related constructs with the Complete Expectation Disconfirmation Theory (EDT) Model. Our results demonstrate that trust plays a central role in the EDT process and that the EDT process helps explain trust’s role more completely. The study shows that technology trusting expectations influence trusting intention through performance, disconfirmation, and satisfaction. We also show that technology trusting intention adds predictive power to EDT’s satisfaction construct as together they predict usage continuance intention. For research, our results provide a strong combined EDT and trust theory base for future studies that examine expectation management and system development projects. For practice, our study informs systems implementation strategies for technologies that have fewer human-like characteristics and more technology-like characteristics. Our findings underscore that managers need to adopt an EDT process-based view when seeking to build trust, satisfaction, and continuance intention in strategically important information systems.     

Incorporating behavioral trust theory into system development for ubiquitous applications

Trust has been shown to be a key factor for technology adoption by users, that is, users prefer to use applications they trust. While existing literature on trust originating in computer science mostly revolves around aspects of information security, authentication, etc., research on trust in automation—originating from behavioral sciences—almost exclusively focuses on the sociotechnical context in which applications are embedded. The behavioral theory of trust in automation aims at explaining the formation of trust, helping to identify countermeasures for users’ uncertainties that lead to lessened trust in an application. We hence propose an approach to augment the system development process of ubiquitous systems with insights into behavioral trust theory. Our approach enables developers to derive design elements that help foster trust in their application by performing four key activities: identifying users’ uncertainties, linking them to trust antecedents from theory, deducting functional requirements and finally designing trust-supporting design elements (TSDEs). Evaluating user feedback on two recommender system prototypes, gathered in a study with over 160 participants, we show that by following our process, we were able to derive four TSDEs that helped to significantly increase the users’ trust in the system.     

Factors affecting the smartphone users to use the mobile portal services: focusing on Korean mobile portals

This study aims to clarify whether and how the portal sites in wired Internet environments can enhance their positions as market leaders in the mobile environment.The result may explain that the user’s trust in the mobile services of portal sites (the mobile portal services) is related to the site’s quality and the site’s brand equity significantly and is a mediator to increase the user’s intention to use mobile portal services. This study also explains that the user’s experience with a smartphone can encourage portal users to expect that the mobile portal services are useful and that the experience can link brand equity in certain business environments to trust in the brand in other business environments. Site quality including the design quality of portal sites can affect the user’s trust in the mobile services of the portal sites directly and can affect the user’s intention to use the mobile portal services under certain conditions. Practically, this study suggests that portal sites should focus on designing and developing more usable sites with high-quality UI components—convenient menu navigation, a proper UI design, and usable content rather depend on their current position as the leading companies in the wired Internet environment.     

Evaluating recommender systems from the user’s perspective: survey of the state of the art

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users?? perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS??s ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users?? adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system??s recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system??s overall perceptive qualities and how these qualities influence users?? behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing methods.     

TruBeRepec: a trust-behavior-based reputation and recommender system for mobile applications

Mobile applications are software packages that can be installed and executed in a mobile device. Which mobile application is trustworthy for a user to purchase, download, install, execute or recommend becomes a crucial issue that impacts its final success. This paper proposes TruBeRepec, a trust-behavior-based reputation and recommender system for mobile applications. We explore a model of trust behavior for mobile applications based on the result of a large-scale user survey. We further develop a number of algorithms that are used to evaluate individual user’s trust in a mobile application through trust behavior observation, generate the application’s reputation by aggregating individual trust and provide application recommendations based on the correlation of trust behaviors. We show the practical significance of TruBeRepec through simulations and analysis with regard to effectiveness, robustness, and usability, as well as privacy.     

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.