Implicit feedback techniques on recommender systems applied to electronic books

The goal of this research is to define and capture a series of parameters that allowed us to perform a comparative analysis and find correlations between explicit and implicit feedback on recommender systems. Most of these systems require explicit actions from the users, such as rating, and commenting. In the context of electronic books this interaction may alter the patterns of reading and understanding of the users, as they are asked to stop reading and rate the content. By simulating the behavior of an electronic book reader we have improved the feedback process, by implicitly capturing, measuring, and classifying the information needed to discover user interests. In these times of information overload, we can now develop recommender systems that are mostly based on the user’s behavior, by relying on the obtained results.     

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.     

基于显式与隐式反馈信息的概率矩阵分解推荐

针对现有的基于用户显式反馈信息的推荐系统推荐准确率不高的问题,提出了一种基于显式与隐式反馈信息的概率矩阵分解推荐方法。该方法综合考虑了显示反馈信息和隐式反馈信息,在对用户信任关系矩阵和商品评分矩阵进行概率分解的同时加入了用户评分记录的隐式反馈信息,优化训练模型参数,为用户提供精确的预测评分。实验结果表明,该方法可以有效地获得用户偏好,产生大量的准确度高的推荐。     

A time-based approach to effective recommender systems using implicit feedback

Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations using explicit ratings on items from users. In some e-commerce environments, however, it is difficult to collect explicit feedback data; only implicit feedback is available.

In this paper, we present a method of building an effective collaborative filtering-based recommender system for an e-commerce environment without explicit feedback data. Our method constructs pseudo rating data from the implicit feedback data. When building the pseudo rating matrix, we incorporate temporal information such as the user’s purchase time and the item’s launch time in order to increase recommendation accuracy.

Based on this method, we built both user-based and item-based collaborative filtering-based recommender systems for character images (wallpaper) in a mobile e-commerce environment and conducted a variety of experiments. Empirical results show our time-incorporated recommender system is significantly more accurate than a pure collaborative filtering system.     

EARS: Emotion-aware recommender system based on hybrid information fusion

Recommender systems suggest items that users might like according to their explicit and implicit feedback information, such as ratings, reviews, and clicks. However, most recommender systems focus mainly on the relationships between items and the user’s final purchasing behavior while ignoring the user’s emotional changes, which play an essential role in consumption activity. To address the challenge of improving the quality of recommender services, this paper proposes an emotion-aware recommender system based on hybrid information fusion in which three representative types of information are fused to comprehensively analyze the user’s features: user rating data as explicit information, user social network data as implicit information and sentiment from user reviews as emotional information. The experimental results verify that the proposed approach provides a higher prediction rating and significantly increases the recommendation accuracy.     

融合用户评分与显隐兴趣相似度的协同过滤推荐算法

协同过滤算法是推荐系统中使用最广泛的算法,其核心是利用某兴趣爱好相似的群体来为用户推荐感兴趣的信息。传统的协同过滤算法利用用户-项目评分矩阵计算相似度,通过相似度寻找用户的相似群体来进行推荐,但是由于其评分矩阵的稀疏性问题,对相似度的计算不够准确,这间接导致推荐系统的质量下降。为了缓解数据稀疏性对相似度计算的影响并提高推荐质量,提出了一种融合用户评分与用户显隐兴趣的相似度计算方法。该方法首先利用用户-项目评分矩阵计算用户评分相似度;然后根据用户基本属性与用户-项目评分矩阵得出项目隐性属性;之后综合项目类别属性、项目隐性属性、用户-项目评分矩阵和用户评分时间,得到用户显隐兴趣相似度;最后融合用户评分相似度和用户显隐兴趣相似度得到用户相似度,并以此相似度寻找用户的相似群体以进行推荐。在数据集Movielens上的实验结果表明,相比传统算法中仅使用单一的评分矩阵来计算相似度,提出的新相似度计算方法不仅能够更加准确地寻找到用户的相似群体,而且还能够提供更好的推荐质量。     

应用WUM和RBFN补值的协同过滤推荐研究

协同过滤是目前推荐系统中最为成功的一种方法,但面临稀疏数据特征时存在冷启动、稀疏性、可扩展性等问题。提出利用Web数据挖掘（WUM）获取隐性数据对显性用户评价矩阵进行补值,应用径向基函数（RBFN）对补值后的评价矩阵进一步进行平滑处理,得到消除稀疏性后的完全评价矩阵,基于完全评价矩阵利用协同过滤技术对相似用户进行聚类并实施推荐。实验评价结果表明该方法与传统协同过滤推荐方法相比,无论在推荐精度还是推荐相关性上都更为有效。     

Investigating the influence of QoS on personal evaluation behaviour in a mobile context

The efficiency of personal video suggestions generated by recommender systems is highly dependent on the quality of the obtained user feedback. This feedback has to reflect the personal interest in the content of the viewed video, to obtain accurate results. However, user feedback might undesirably be influenced by additional aspects such as the loading speed or the quality of the video. To date, this issue has received very little research attention. Therefore, this study investigates the direct influence of audio-visual quality parameters on explicit user feedback for the first time to our knowledge via a mobile, Living Lab experiment. This paper proposes a feedback model which takes the Quality of Service (QoS) parameters of the mobile network into account. This model can be used as an additional feedback filter for video recommendation systems that could help to eliminate the influences of QoS on explicit user feedback.     

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.     

基于信任机制下概率矩阵分解的用户评分预测

互联网的蓬勃发展,在为用户提供便利的同时,其海量信息也为用户选择造成了困难,基于用户理解的信息推荐服务正成为应时之需.相较于面向单个用户信息的传统推荐技术,基于社交信息的推荐技术通过引入影响力建模,可以更真实地还原用户属性及行为.然而,已有的社交推荐技术往往停留于对用户影响的笼统归纳,并没有对其内在机制进行清晰分类和量化.针对这一问题,通过对用户评分行为中的信任关系进行分析,着重研究了信任用户间接影响用户偏好和直接影响用户评分两种不同机制,进而提出了基于用户间信任关系融合建模的概率矩阵分解模型TPMF,从而实现对上述两种机制的有效融合.在此基础之上,针对不同用户受两种机制影响权重不同的问题,通过借助评分相关性对用户进行聚类并映射到相应权重,实现了用户模型参数的个性化选择.公开数据集的多项实验结果表明：提出的TPMF及其衍生算法在各项指标上优于现有代表性算法,验证了所提出的影响机制及技术框架的有效性.     

基于用户实时反馈的协同过滤算法

传统的基于内存的协同过滤算法存在可扩展性不足的问题,而基于模型的协同过滤算法由于模型数据的滞后,造成推荐质量不高。针对以上情况,提出一种基于用户实时反馈的协同过滤算法,该算法在用户提交项目评分之后能实现对推荐模型数据的实时更新,从而更精确地反映用户的兴趣变化。实验结果表明,该算法能够有效地提高推荐精确度并且大幅地缩短了推荐时间。     

新的基于日志分析的用户个性化模型

作为个性化服务技术的核心,用户模型的质量关系到个性化服务的质量。目前的用户模型大多只考虑用户的显式信息或隐式信息,很少同时考虑两者,使得检索质量不如人意。提出了一种新的基于日志分析的用户个性化模型,结合了传统的显式建模和隐式建模的优点,把显式个性化信息和隐式个性化信息通过两层树状结构结合起来。模型同时考虑了用户历史信息的长短期划分,以及检索系统返回结果的顺序和用户对结果页面的点击顺序。实验结果表明,基于该用户模型的个性化检索效果与原有检索系统的检索效果相比有显著提高。     

融合元数据及隐式反馈信息的多层次联合学习推荐方法

基于隐式反馈信息的推荐是目前推荐系统领域的重要方法,能在一定程度上解决显式信息难以获得的问题。但由于隐式数据本身的特点单纯利用隐式反馈信息往往难以获取较好的推荐性能。针对此问题,本文提出一种融合元数据及隐式反馈信息的多层次深度联合学习（Multi-level Deep Joint Learning,简称MDJL）的推荐方法。它利用双深度神经网络共同学习,其中一个网络利用隐式反馈学习用户及项目个体个性化关系,另一个网络利用元数据学习高层次群体共性化关系,从而有效地表达用户偏好,使MDJL框架在个体及群体因素间达到平衡。实验结果表明,MDJL推荐算法在MovieLens 100K和MovieLens 1M两个公开数据集上均表现出更优越的推荐性能。     

Personalized web-document filtering using reinforcement learning

Document filtering is increasingly deployed in Web environments to reduce information overload of users. We formulate online information filtering as a reinforcement learning problem, i.e., TD(0). The goal is to learn user profiles that best represent information needs and thus maximize the expected value of user relevance feedback. A method is then presented that acquires reinforcement signals automatically by estimating user's implicit feedback from direct observations of browsing behaviors. This "learning by observation" approach is contrasted with conventional relevance feedback methods which require explicit user feedbacks. Field tests have been performed that involved 10 users reading a total of 18,750 HTML documents during 45 days. Compared to the existing document filtering techniques, the proposed learning method showed superior performance in information quality and adaptation speed to user preferences in online filtering.     

Recommender Systems Research: A Connection-Centric Survey

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.     

Extracting relevance and affect information from physiological text annotation

We present physiological text annotation, which refers to the practice of associating physiological responses to text content in order to infer characteristics of the user information needs and affective responses. Text annotation is a laborious task, and implicit feedback has been studied as a way to collect annotations without requiring any explicit action from the user. Previous work has explored behavioral signals, such as clicks or dwell time to automatically infer annotations, and physiological signals have mostly been explored for image or video content. We report on two experiments in which physiological text annotation is studied first to (1) indicate perceived relevance and then to (2) indicate affective responses of the users. The first experiment tackles the user’s perception of relevance of an information item, which is fundamental towards revealing the user’s information needs. The second experiment is then aimed at revealing the user’s affective responses towards a -relevant- text document. Results show that physiological user signals are associated with relevance and affect. In particular, electrodermal activity was found to be different when users read relevant content than when they read irrelevant content and was found to be lower when reading texts with negative emotional content than when reading texts with neutral content. Together, the experiments show that physiological text annotation can provide valuable implicit inputs for personalized systems. We discuss how our findings help design personalized systems that can annotate digital content using human physiology without the need for any explicit user interaction.     

面向用户偏好建模的个性化对话推荐算法

推荐系统的目标是从物品数据库中，选择出与用户兴趣偏好相匹配的子集，缓解用户面临的“信息过载”问题。因而近年来推荐系统越来越多地应用到电商、社交等领域，展现出巨大的商业潜力。传统推荐系统中，系统对用户的认知往往来源于历史交互记录，例如点击率或者购买记录，这是一种隐式用户反馈。对话推荐系统能够通过自然语言与用户进行多轮对话，逐步深入挖掘其兴趣偏好，从而向对方提供高质量的推荐结果。相比于传统推荐系统，对话推荐系统主要有两方面的不同。其一，对话推荐系统能够利用自然语言与用户进行语义上连贯的多轮对话，提升了人机交互中的用户体验；其二，系统能够询问特定的问题直接获取用户的显式反馈，从而更深入地理解用户兴趣偏好，提供更可靠的推荐结果。目前已经有不少工作在不同的问题设定下对该领域进行了探索，然而尽管如此，这些工作仍仅局限于关注当前正在进行的对话，忽视了过去交互记录中蕴涵的丰富信息，导致对用户偏好建模的不充分。为了解决这个问题，本文提出了一个面向用户偏好建模的个性化对话推荐算法框架，通过双线性模型注意力机制与自注意力层次化编码结构进行用户偏好建模，从而完成对候选物品的排序与推荐。本文设计的模型结构能够在充分利用用户历史对话信息的同时，权衡历史对话与当前对话两类数据的重要性。丰富的用户相关信息来源使得推荐结果在契合用户个性化偏好的同时，更具备多样性，从而缓解“信息茧房”等现象带来的不良影响。基于公开数据集的实验表明了本文方法在个性化对话推荐任务上的有效性。     

The effects of transparency on trust in and acceptance of a content-based art recommender

The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study’s results.

User interest modeling for social TV-recommender systems based on audiovisual consumption

This paper presents an innovative consumption-modeling system applied to digital TV, able to infer the user interest regarding the TV contents. The user interest inferred is used to feed content recommender systems, and it has been tested in real scenarios involving real users. The modeling system uses as input the TV consumption data and performs an algorithm based on a Hidden Markov Model and Bayesian inference techniques to infer the said user interest. Real data have been picked up in real time to feed our modeling, and the final results have been checked comparing with user’s tastes, which have been expressed through a set of questionnaires and the whole system has been tested in a TV broadcasting scenario with real users. Conclusions show that our system improves the reliability from classic user interest modeling systems (they are mainly based on explicit opinion based methods, which can be intrusive for general users and could show certain deceptive results, as it is described in this paper).     

Task and user effects on reading patterns in information search

We report on an investigation into people’s behaviors on information search tasks, specifically the relation between eye movement patterns and task characteristics. We conducted two independent user studies (n = 32 and n = 40), one with journalism tasks and the other with genomics tasks. The tasks were constructed to represent information needs of these two different users groups and to vary in several dimensions according to a task classification scheme. For each participant we classified eye gaze data to construct models of their reading patterns. The reading models were analyzed with respect to the effect of task types and Web page types on reading eye movement patterns. We report on relationships between tasks and individual reading behaviors at the task and page level. Specifically we show that transitions between scanning and reading behavior in eye movement patterns and the amount of text processed may be an implicit indicator of the current task type facets. This may be useful in building user and task models that can be useful in personalization of information systems and so address design demands driven by increasingly complex user actions with information systems. One of the contributions of this research is a new methodology to model information search behavior and investigate information acquisition and cognitive processing in interactive information tasks.