A survey on personality-aware recommendation systems

With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personality-aware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.

     

基于文本注意力的推荐系统可解释性研究

可解释性能够提高用户对推荐系统的信任度并且提升推荐系统的说服力和透明性,因此有许多工作都致力于实现推荐系统的可解释性。由于评论中包含了丰富的信息,能够体现用户偏好与情感信息,同时包含了对应商品所具有的特性,最近的一些基于评论的深度推荐系统有效地提高了推荐系统的可解释性。这些基于评论的深度推荐系统中内置的注意力机制能够从对应的评论中识别出有用的语义单元（例如词、属性或者评论）,而推荐系统通过这些高权重的语义单元做出决策,从而增强推荐系统的可解释性。但可解释性在很多工作中仅作为一个辅助性的子任务,只在一些案例研究中来做出一些定性的比较,来说明推荐系统是具有可解释性的,到目前为止并没有一个能够综合地评估基于评论推荐系统可解释性的方法。本文首先根据在注意力权重计算机制的不同,将这些具有可解释性的基于评论的推荐系统分为三类：基于注意力的推荐系统,基于交互的推荐系统,基于属性的推荐系统,随后选取了五个最先进的基于评论的深度推荐系统,通过推荐系统内置的注意力机制获得的评论权重文档,在三个真实数据集上进行了人工标注,分别量化地评价推荐系统的可解释性。标注的结果表明不同的基于评论的深度推荐系统的可解释性是具有优劣之分的,但当前的基于评论的深度推荐系统都有超过一半的可能性能够捕捉到用户对目标评论的偏好信息。在评估的五个推荐系统中,并没有哪个推荐系统在所有的数据中具有绝对的优势。也就是说,这些推荐系统在推荐可解释性方面是相互补充的。通过进一步的数据分析发现,如果推荐系统具有更精确的分数预测结果,那推荐系统通过注意力机制获得的高权重的信息确实更能够体现用户的偏好或者商品特征,说明推荐系统内置的注意力机制在提高可解释性的同时也能够提高预测精度;并且发现相较于长评论,推荐系统更容易捕捉到较短的评论中的特征信息;而可解释性评分高的推荐系统会更可能地为形容词赋予较高的权重。本文也为推荐系统可解释性评估进一步研究和探索更好的基于评论的推荐系统解决方案提供了一些启示。     

知识图谱的推荐系统综述

如何为用户提供个性化推荐并提高推荐的准确度和用户满意度,是当前推荐系统研究面临的主要问题。知识图谱的出现为推荐系统的改进提供了新的途径。本文研究了知识图谱近年来在推荐系统中的应用情况,从基于本体的推荐生成、基于开放链接数据的推荐生成以及基于图嵌入的推荐生成3个方面对研究现状进行了综述。在此基础上,提出了基于知识图谱的推荐系统总体框架,分析了其中涉及的关键技术,并对目前存在的重点和难点问题进行了讨论,指出了下一步需要开展的研究工作。     

知识驱动的推荐系统：现状与展望

个性化推荐系统能够根据用户的个性化偏好和需要,自动、快速、精准地为用户提供其所需的互联网资源,已成为当今大数据时代应用最广泛的信息检索系统,具有巨大的商业应用价值。近年来,随着互联网海量数据的激增,人工智能技术的快速发展与普及,以知识图谱为代表的大数据知识工程日益受到学界和业界的高度关注,也有力地推动推荐系统和个性化推荐技术也迈入到知识驱动与赋能的发展阶段。将知识图谱中蕴含的丰富知识作为有用的辅助信息引入推荐系统,不仅能够有效应对数据稀疏、语义失配等传统推荐系统难以避免的问题,还能帮助推荐系统产生多样化、可解释的推荐结果,并更好地完成跨领域推荐、序列化推荐等具有挑战性的推荐任务,从而提升各类实际推荐场景中的用户满意度。本文将现有融入知识图谱的各种推荐模型按其采用的推荐算法与面向的推荐场景不同进行分类,构建科学、合理的分类体系。其中,按照推荐方法的不同,划分出基于特征表示的和基于图结构的两大类推荐模型;按推荐场景划分,特别关注多样化推荐、可解释推荐、序列化推荐与跨领域推荐。然后,我们在各类推荐模型中分别选取代表性的研究工作进行介绍,还简要对比了各个模型的特点与优劣。此外,本文还结合当下人工智能技术和应用的发展趋势,展望了认知智能推荐系统的发展前景,具体包括融合多模态知识的推荐系统,具有常识理解能力的推荐系统,以及解说式、劝说式、抗辩式推荐系统。本文的综述内容和展望可作为推荐系统未来研究方向的有益参考。     

Preference-based user rating correction process for interactive recommendation systems

In most of the recommendation systems, user rating is an important user activity that reflects their opinions. Once the users return their ratings about items the systems have suggested, the user ratings can be used to adjust the recommendation process.However, while rating the items users can make some mistakes (e.g., natural noises). As the recommendation systems receive more incorrect ratings, the performance of such systems may decrease. In this paper, we focus on an interactive recommendation system which can help users to correct their own ratings. Thereby, we propose a method to determine whether the ratings from users are consistent to their own preferences (represented as a set of dominant attribute values) or not and eventually to correct these ratings to improve recommendation. The proposed interactive recommendation system has been particularly applied to two user rating datasets (e.g., MovieLens and Netflix) and it has shown better recommendation performance (i.e., lower error ratings).     

应用知识图谱的推荐方法与系统

数据稀疏和冷启动是当前推荐系统面临的两大挑战. 以知识图谱为表现形式的附加信息能够在某种程度上缓解数据稀疏和冷启动带来的负面影响, 进而提高推荐的准确度. 本文综述了最近提出的应用知识图谱的推荐方法和系统, 并依据知识图谱来源与构建方法、推荐系统利用知识图谱的方式, 提出了应用知识图谱的推荐方法和系统的分类框架, 进一步分析了本领域的研究难点. 本文还给出了文献中常用的数据集. 最后讨论了未来有价值的研究方向.     

Common structure and properties of filtering systems

Recommendation systems have been studied actively since the 1990s. Generally, recommendation systems choose one or more candidates from a set of candidates through a filtering process. Methods of filtering can be divided into two categories: collaborative filtering, in which candidates are chosen based on choices of other persons whose interests or tastes are similar, and content-based filtering, in which items are chosen based on the profile or action history of the recommendee. However, these methods share the same structure in the sense that both of them recommend items based on relevance degrees of items and references, as well as relevance degrees between the recommendee and each reference. Most discussions about recommendation systems focus on the methods of choosing recommended candidates; few focus on foundational concepts of recommendation conditions that systems must satisfy, and problems that current systems have compared with these conditions. In this paper, recommendation systems are reconsidered from the viewpoint of multi-criteria decision making. Conventional filtering methods (e.g., collaborative filtering and content-based filtering) are formulated as linear weighted sum type recommendation systems. Several properties of linear weighted sum type recommendation systems are identified and formulated from the viewpoint of voting.     

个性化推荐系统隐私保护策略研究进展

个性化推荐系统能较好地帮助用户获得个人所需的信息,但它要获得好的推荐效果,需要收集大量的用户个人信息;这些信息可能泄露个人隐私,用户会因对隐私泄露的担心而放弃对推荐系统的信任,所以大量的研究集中于如何在获得高效推荐的同时保护用户的个人隐私。主要就个性化推荐系统中使用的隐私保护技术进行了综述,在给出了隐私和隐私保护定义的同时讨论了隐私保护的相关技术,包括隐私策略描述语言和目前使用的隐私保护技术。最后尝试给出了今后的研究重点和方向。     

Resource recommendation in social annotation systems: A linear-weighted hybrid approach

Social annotation systems enable the organization of online resources with user-defined keywords. Collectively these annotations provide a rich information space in which users can discover resources, organize and share their finds, and connect to other users with similar interests. However, the size and complexity of these systems can lead to information overload and reduced utility for users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. To date, most efforts have concentrated on the problem of tag recommendation – personalized suggestions for possible annotations. Resource recommendation has not received the same systematic evaluation, in part because the task is inherently more complex. In this article, we provide a general formulation for the problem of resource recommendation in social annotation systems that captures these variants, and we evaluate two cases: basic resource recommendation and tag-specific resource recommendation. We also propose a linear-weighted hybrid framework for resource recommendation. Using six real-world datasets, we show that its integrative approach is essential for this recommendation task and provides the most adaptability given the varying data characteristics in different social annotation systems. We find that our algorithm is more effective than other more mathematically-complex techniques and has the additional advantages of flexibility and extensibility.     

New Recommendation Techniques for Multicriteria Rating Systems

Personalization technologies and recommender systems help online consumers avoid information overload by making suggestions regarding which information is most relevant to them. Most online shopping sites and many other applications now use recommender systems. Two new recommendation techniques leverage multicriteria ratings and improve recommendation accuracy as compared with single-rating recommendation approaches. Taking full advantage of multicriteria ratings in personalization applications requires new recommendation techniques. In this article, we propose several new techniques for extending recommendation technologies to incorporate and leverage multicriteria rating information.     

一种改进的偏好融合组推荐方法

近年来,组推荐系统已经逐渐成为推荐系统领域的研究热点之一.在电影电视和旅游推荐中,用户常常是参与活动的一组人,这就需要为多个用户形成的群组进行推荐.作为解决群组推荐问题的有效手段,组推荐系统将单个用户推荐扩展为群组推荐,目前已经应用在新闻、音乐、电影、餐饮等诸多领域.现有的组推荐融合方法主要是模型融合与推荐融合,其效用好坏目前仍没有定论,并且它们各有自己的优缺点.模型融合存在着群组成员间的公平性问题,推荐融合忽视了群组成员间的交互.提出一种改进的偏好融合组推荐方法,它结合了两种融合方法的优点.同时根据实验得出了"群组偏好与个人偏好具有相似性"的结论,并将它结合在改进方法中.最后,通过在Movielens数据集上的实验分析,验证了该方法的有效性,证明了它能够有效地提高推荐准确率.     

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。     

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.     

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。     

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.     

社会化推荐研究进展

文章提供了一个关于社会化推荐研究进展的概述。随着推荐系统研究的不断深入,将社会化影响融入推荐系统成为一个新的研究热点和问题丰富的研究领域。首先描述了社会化推荐的相关技术：推荐系统和社会化网络分析。对当前社会化推荐的一些最新技术方法进行分类介绍,具体包括利用社会化关系推荐物品,利用社会化关系推荐好友,根据内容推荐社会化关系,小组推荐和为团体推荐五个方面。     

一种基于中间代理的个性化推荐系统

个性化推荐系统能很好地解决互联网中信息过载的问题,传统推荐系统存在着商家较为分散、隐私容易泄漏的问题。提出了一种基于中间代理的电子商务智能推荐系统,利用内容过滤技术进行推荐,在考虑用户隐私的基础上使用向量空间模型挖掘用户的兴趣偏好和商品的特征评价,引入时间遗忘函数以处理兴趣变化问题,根据收集的信息产生推荐序列,针对重点难点问题提出了解决方案。采用Movielens数据集进行的实验结果表明,该方法能提供较好的推荐准确度与计算性能。     

融合粗糙集与GRA的异构信息多准则三支推荐及其在医疗推荐中的应用

在临床实践中,医疗推荐可能存在数据多源异构和推荐项目多准则的问题,考虑到医疗推荐的这些特征,定义了异构信息系统上不同数据类型的距离测度,实现多源异构数据的有效处理.首先,根据两个对象之间的混合距离得到异构信息系统中的二元关系,并构建异构信息粗糙集模型;然后,将多准则推荐与多准则决策方法(MCDM)相结合,运用灰色关联分析(GRA)聚合每个项目下多准则评分将其转化为单评分推荐;最后,在异构信息粗糙集模型的基础上引入三支决策,同时基于协同过滤方法实现三支推荐,考虑了推荐过程中的决策成本.在医疗应用部分采用临床数据实验,验证了所提出的模型能够为临床诊断提供知识支持,有效降低推荐决策成本,提高推荐的准确性.     

A Multicriteria Collaborative Filtering Approach Using the Indifference Relation and its Application to Initiator Recommendation for Group-Buying

For recommender systems, the main aim of the popular collaborative filtering approaches is to recommend items that users with similar preferences have liked in the past. Single-criterion recommender systems have been successfully used in several applications. Because leveraging multicriteria information can potentially improve recommendation accuracy, multicriteria rating systems that allow users to assign ratings to various content attributes of items they have consumed have become the focus in recommendation systems. By treating the recommendation of items as a multicriteria decision problem, it is interesting to incorporate the preference relation of users of multicriteria decision making (MCDM) into the similarity measure for a collaborative filtering approach. For this, the well-known indifference relation can justify a discrimination or similarity between any two users, if outranking relation theory is incorporated. The applicability of the proposed single-criterion and multicriteria recommendation approaches to the recommendation of initiators on a group-buying website was examined. Experimental results have demonstrated that the generalization ability of the proposed multicriteria recommendation approach performs well in comparison to other single-criterion and multicriteria collaborative filtering approaches.     

基于领域最近邻的协同过滤推荐算法

协同过滤是目前电子商务推荐系统中广泛应用的最成功的推荐技术,但面临严峻的用户评分数据稀疏性和推荐实时性挑战.针对上述问题,提出了基于领域最近邻的协同过滤推荐算法,以用户评分项并集作为用户相似性计算基础,将并集中的非目标用户区分为无推荐能力和有推荐能力两种类型;对于前一类用户不再计算用户相似性以改善推荐实时性,对于后一类用户则提出"领域最近邻"方法对并集中的未评分项进行评分预测,从而降低数据稀疏性和提高最近邻寻找准确性.实验结果表明,该算法能有效提高推荐质量.