The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.     

图表示学习短视频智能推荐研究综述

随着短视频数量的爆发式增长, 精准的个性化短视频推荐成为学术界和工业界的迫切需求。然而,现有的推荐方法没有考虑实际的短视频具有数据多源异构多模态、用户行为复杂多样、用户兴趣动态变化等特点。短视频模态间的语义鸿沟、社交网络用户多行为挖掘、用户动态兴趣捕捉依然是短视频推荐领域面临的三个重要问题。针对当前推荐系统存在的问题,并充分考虑短视频推荐系统的实际需求,本文介绍了短视频推荐中基于图表示学习的短视频推荐方法;研究了短视频异构多模态特征表示,充分挖掘视频内容特征并进行高效融合;研究了短视频社交网络用户多行为表示,通过社交网络用户多种行为挖掘更细粒度的用户偏好;研究了用户的动态偏好表示方法,通过利用时序信息建模用户的动态兴趣,保证推荐结果的准确度并增加其多样性与个性化。本研究可在理论和实践上推进基于图特征学习的短视频推荐研究,也可作为短视频推荐系统的关键技术。     

OMUS: an optimized multimedia service for the home environment

Media content in home environments is often scattered across multiple devices in the home network. As both the available multimedia devices in the home (e.g., smartphones, tablets, laptops, game consoles, etc.) and the available content (video and audio) is increasing, interconnecting desired content with available devices is becoming harder and home users are experiencing difficulties in selecting interesting content for their current context. In this paper, we start with an analysis of the home environment by means of a user study. Information handling problems are identified and requirements for a home information system formulated. To meet these requirements we propose the OMUS home information system which includes an optimized content aggregation framework, a hybrid group-based contextual recommender system, and an overall web-based user interface making both content and recommendations available for all devices across the home network. For the group recommendations we introduced distinct weights for each user and showed that by varying the weights, the coverage (i.e., items that can be returned by the recommender) considerably increases. Also the addition of genre filter functionality was proven to further boost the coverage. The OMUS system was evaluated by means of focus groups and by qualitative and quantitative performance assessment of individual parts of the system. The modularity of internal components and limited imposed hardware requirements implies flexibility as to how the OMUS system can be deployed (ranging from e.g., embedded in hardware devices or more software services based).     

CAESAR: context-aware explanation based on supervised attention for service recommendations

Explainable recommendations have drawn more attention from both academia and industry recently, because they can help users better understand recommendations (i.e., why some particular items are recommended), therefore improving the persuasiveness of the recommender system and users’ satisfaction. However, little work has been done to provide explanations from the angle of a user’s contextual situations (e.g., companion, season, and destination if the recommendation is a hotel). To fill this research gap, we propose a new context-aware recommendation algorithm based on supervised attention mechanism (CAESAR), which particularly matches latent features to explicit contextual features as mined from user-generated reviews for producing context-aware explanations. Experimental results on two large datasets in hotel and restaurant service domains demonstrate that our model improves recommendation performance against the state-of-the-art methods and furthermore is able to return feature-level explanations that can adapt to the target user’s current contexts.

     

Ontology-Based User Modeling in an Augmented Audio Reality System for Museums

Ubiquitous computing is a challenging area that allows us to further our understanding and techniques of context-aware and adaptive systems. Among the challenges is the general problem of capturing the larger context in interaction from the perspective of user modeling and human–computer interaction (HCI). The imperative to address this issue is great considering the emergence of ubiquitous and mobile computing environments. This paper provides an account of our addressing the specific problem of supporting functionality as well as the experience design issues related to museum visits through user modeling in combination with an audio augmented reality and tangible user interface system. This paper details our deployment and evaluation of ec(h)o – an augmented audio reality system for museums. We explore the possibility of supporting a context-aware adaptive system by linking environment, interaction objects and users at an abstract semantic level instead of at the content level. From the user modeling perspective ec(h)o is a knowledge-based recommender system. In this paper we present our findings from user testing and how our approach works well with an audio and tangible user interface within a ubiquitous computing system. We conclude by showing where further research is needed.     

基于用户偏好融合的组推荐算法综述

传统的推荐系统是面向单个用户的推荐。作为个性化推荐的一个新的延伸,目前有越来越多的推荐系统正试图面向一组成员进行推荐。将推荐对象从单个用户扩展到一组用户的转变带来了许多新的课题,该文将主要介绍目前已有的几种组推荐算法,并总结一般组推荐系统的偏好融合过程。     

旅游推荐系统研究综述

为用户提供个性化推荐服务并提高推荐的准确度和用户满意度,是当前旅游推荐系统的主要研究任务。文中分析了旅游推荐系统与传统推荐系统的异同点,并从基于内容的推荐、基于协同过滤的推荐、基于知识的推荐、基于人口统计的推荐、混和型推荐以及基于位置感知的推荐共6个方面考查了旅游推荐的研究现状。在此基础上,给出了旅游推荐系统的一个总体框架。最后,总结分析了旅游推荐系统面临的6个重点和难点问题,并指出了下一步需要关注的研究方向。     

Explaining the user experience of recommender systems

Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively). Furthermore, it incorporates the influence of personal and situational characteristics on the user experience. This paper reviews how current literature maps to the framework and identifies several gaps in existing work. Consequently, the framework is validated with four field trials and two controlled experiments and analyzed using Structural Equation Modeling. The results of these studies show that subjective system aspects and experience variables are invaluable in explaining why and how the user experience of recommender systems comes about. In all studies we observe that perceptions of recommendation quality and/or variety are important mediators in predicting the effects of objective system aspects on the three components of user experience: process (e.g. perceived effort, difficulty), system (e.g. perceived system effectiveness) and outcome (e.g. choice satisfaction). Furthermore, we find that these subjective aspects have strong and sometimes interesting behavioral correlates (e.g. reduced browsing indicates higher system effectiveness). They also show several tradeoffs between system aspects and personal and situational characteristics (e.g. the amount of preference feedback users provide is a tradeoff between perceived system usefulness and privacy concerns). These results, as well as the validated framework itself, provide a platform for future research on the user-centric evaluation of recommender systems.     

Recommendation-based editor for business process modeling

To ensure proper and efficient modeling of business processes, it is important to support users of process editors adequately. With only minimal modeling support, the productivity of novice business process modelers may be low when starting process modeling. In this article, we present a theoretically sound and empirically validated recommendation-based modeling support system, which covers different aspects of business process modeling. We consider basic functionality, such as an intuitive search interface, as well as advanced concepts like patterns observed in other users' preferences. Additionally, we propose a multitude of interaction possibilities with the recommendation system, e.g., different metrics that can be used in isolation or an overall recommender component that combines several sub metrics into one comprehensive score. We validate a prototype implementation of the recommendation system with exhaustive user experiments based on real-life process models. To our knowledge, this is the only comprehensive recommendation system for business process modeling that is available.     

基于改进Canopy聚类的协同过滤推荐算法

推荐系统通过建立用户和信息产品之间的二元关系,利用用户行为产生的数据挖掘每个用户感兴趣的对象并进行推荐,基于用户的协同过滤是近年来的主流办法,但存在一定局限性：推荐时需要考虑全部用户,而单个用户往往只与少部分用户类似。为了解决这个问题,提出了基于改进Canopy聚类的协同过滤推荐算法,将用户模型数据密度、距离与用户活跃度结合,计算用户数据权值,对用户模型数据进行聚类。由于结合了Canopy的聚类思想,同一用户可以属于不同的类,符合用户可能对多领域感兴趣的情况。最后对每个Canopy中的用户进行相应的推荐,根据聚类结果与用户评分预测用户可能感兴趣的对象。通过在数据集MovieLens和million songs上与对比算法进行MAE、RMSE、NDGG三个指标的比较,验证了该算法能显著提高推荐系统预测与推荐的准确度。     

Creating recommendations on electronic books: A collaborative learning implicit approach

Recommender systems appear among other reasons with the purpose to improve web information overload and ease information recovery. This kind of systems aid users to find contents in a non-difficult way and with minimal effort. Even though, a great number of these systems performance requires contents to be explicitly rated in order to determine user’s interest. When interacting with electronic books this performance may alter users reading and understanding patterns as they are asked to stop reading and rate the content. Therefore, the analysis of user behavior, preferences and reading background can be considered suitable for a recommender system to build collective web knowledge in a collaborative learning context. This way, recommender system can assist users in finding contents of their interest without explicit rating based on previous constructed knowledge. The goal of this research is to propose an architecture to build a content recommendation platform based on eBook reading user behavior, allowing users to learn about the digital content collaboratively. This platform is formed by web readers’ community that aids members in finding contents of their interest in an automatic way and with minimal effort.     

Automatic execution of workflows on laser-scanned data for extracting bridge surveying goals

With the capability of capturing detailed geometry of bridges in minutes, laser scanning technology has attracted the interests of bridge inspectors and researchers in the domain of bridge management. A challenge of effectively utilizing laser scanned point clouds for bridge inspection is that inspectors need to manually extract and measure large numbers of geometric features (e.g., points) for deriving geometric information items (e.g., the minimum underclearance) of bridges, named as bridge surveying goals in this research. Tedious manual data processing impedes inspectors from quantitatively understanding how various data processing options (e.g., algorithms, parameter values) influence the data processing time and the reliabilities of the surveying goal results. This paper shows the needs of automatic workflow executions for extracting surveying goals from laser scanned point clouds, and presents a computational framework for addressing these needs. This computational framework is composed of formal representations of workflows and mechanisms for constructing and executing workflows. Using a prototype system implemented based on this framework, we constructed and quantitatively characterized three workflows for extracting three representative bridge surveying goals, using three metrics of workflow performance defined in this research: exhaustiveness of measurement sampling, reliability of surveying goal results, and time efficiency.     

Enriching one-class collaborative filtering with content information from social media

In recent years, recommender systems have become popular to handle the information overload problem of social media websites. The most widely used Collaborative Filtering methods make recommendations by mining users’ rating history. However, users’ behaviors in social media are usually implicit, where no ratings are available. This is a One-Class Collaborative Filtering (OCCF) problem with only positive examples. How to distinguish the negative examples from missing data is important for OCCF. Existing OCCF methods tackle this by the statistical properties of users’ historical behavior; however, they ignored the rich content information in social media websites, which provide additional evidence for profiling users and items. In this paper, we propose to improve OCCF accuracy by exploiting the social media content information to find the potential negative examples from the missing user-item pairs. Specifically, we get a content topic feature for each user and item by probabilistic topic modeling and embed them into the Matrix Factorization model. Extensive experiments show that our algorithm can achieve better performance than the state-of-art methods.     

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.     

Folksonomy‐Based Recommender Systems: A State‐of‐the‐Art Review

Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open‐ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy‐based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link‐analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines.     

Clayworks: Toward user-oriented software for collaborative modeling and simulation

We consider the development of software systems that integrate collaborative real-time modeling and distributed computing. Our main goal is user-orientation: we need a collaborative workspace for geographically dispersed users with a seamless access of every user to high-performance servers. This paper presents a particular prototype, Clayworks, that allows modeling of virtual clay objects and running computation-intensive deformation simulations for objects crashing into each other. In order to integrate heterogeneous computational resources, we adopt modern Grid middleware and provide the users with an intuitive graphical interface. Simulations are parallelized using a higher-order component (HOC) which abstracts over the web service resource framework (WSRF) used to interconnect our worksuite to the computation server. Clayworks is a representative of a large class of demanding systems which combine collaborative, user-oriented modeling with performance-critical computations, e.g., crash-tests or simulations of biological population evolution.     

一种检测兴趣漂移的图结构推荐系统

协同过滤是构造推荐系统最有效的方法之一.其中,基于图结构推荐方法成为近来协同过滤的研究热点.基于图结构的方法视用户和项为图的结点,并利用图理论去计算用户和项之间的相似度.尽管人们对图结构推荐系统开展了很多的研究和应用,然而这些研究都认为用户的兴趣是保持不变的,所以不能够根据用户兴趣的相关变化做出合理推荐.本文提出一种新的可以检测用户兴趣漂移的图结构推荐系统.首先,设计了一个新的兴趣漂移检测方法,它可以有效地检测出用户兴趣在何时发生了哪种变化.其次,根据用户的兴趣序列,对评分项进行加权并构造用户特征向量.最后,整合二部投影与随机游走进行项推荐.在标准数据集MovieLens上的测试表明算法优于两个图结构推荐方法和一个评分时间加权的协同过滤方法.     

推荐系统的隐私保护研究进展

推荐系统是建立在海量数据挖掘基础之上的一种智能平台,根据用户个人信息与物品特征,比如用户的兴趣、历史购买行为和物品的材质、价格等,利用统计分析和机器学习等人工智能技术建立模型,预测用户对新物品的评价与喜好,从而向用户推荐其可能感兴趣的潜在物品,以实现个性化的信息服务和决策支持.然而,推荐系统的历史数据集、预测模型和推荐结果都与用户的隐私休戚相关,如何能在有效保护用户隐私的前提下,提供正确性可验证的有效推荐结果是一个具有挑战性的重要研究课题.国内外现有的工作多是通过数据扰动或公钥全同态加密技术来试图解决这个问题,但都无法满足推荐系统对高效性、精确性和各类隐私保护的要求.从推荐系统隐私保护的模式、安全模型、轻量级的推荐系统隐私保护一般性构造与推荐结果正确性可验证、可审计等方面,系统阐述了国内外最新研究成果,并在此基础上提出了存在问题、未来研究方向与解决方案.在安全模型方面,聚焦于标准模型或通用组合模型下,用户数据隐私、预测模型隐私和推荐结果隐私等多种安全模型的形式化刻画;在轻量化方面,将不依赖公钥全同态加密技术,通过减少公钥加密/解密次数(最优时一次),在单用户、多数据模型和多用户、多数据模型下,提出高效的推荐系统隐私保护一般性构造方法;最后,通过批量验证技术研究推荐结果轻量化防欺诈与抗抵赖的一般性理论问题.从而,为适用于推荐系统隐私保护的新型加密方案研究及其实用化提供理论和方法支撑.     

Using affective parameters in a content-based recommender system for images

There is an increasing amount of multimedia content available to end users. Recommender systems help these end users by selecting a small but relevant subset of items for each user based on her/his preferences. This paper investigates the influence of affective metadata (metadata that describe the user’s emotions) on the performance of a content-based recommender (CBR) system for images. The underlying assumption is that affective parameters are more closely related to the user’s experience than generic metadata (e.g. genre) and are thus more suitable for separating the relevant items from the non-relevant. We propose a novel affective modeling approach based on users’ emotive responses. We performed a user-interaction session and compared the performance of the recommender system with affective versus generic metadata. The results of the statistical analysis showed that the proposed affective parameters yield a significant improvement in the performance of the recommender system.     

Information Quality Assessment for Facility Management

Assessing the quality of building information models (BIMs) is an important yet challenging task within the construction industry as projects are increasingly being delivered with BIM. This is particularly essential for facility management (FM) users as downstream information consumers that depend on the quality of models developed in the previous project phases. The research presented in this paper addresses this challenge by introducing a framework for information quality assessment (IQA) of BIMs for FM uses. The IQA framework is the outcome of an extensive study of two large owner organizations involving numerous BIM projects. The framework is structured based on the essential FM subjects: assets, spaces, and systems, and the model characteristics: objects, attributes, relationships, and spatial information. The framework is then operationalized through the development and evaluation of information quality (IQ) tests using BIM model checking tools across three projects with different levels of detail and complexity. The proposed IQA framework and associated tests advance the state of knowledge about BIM quality in terms of methods to represent and evaluate conformance to owner requirements.