Guest Editors' Introduction: Recommender Systems

This special issue presents eight articles, five long and three short, on techniques to improve recommender systems. They cover improving such aspects as user interaction with recommenders, the quality of results presented to users, and user trust in presented recommendations. This article is part of a special issue on Recommender Systems.     

Hybrid Recommender Systems: Survey and Experiments

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.     

Tag-Aware Recommender Systems: A State-of-the-Art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.     

协同推荐系统检测模型的一种优化方法

研究表明协同推荐技术容易受到攻击。由于现有的检测模型对低填充规模攻击的检测效果不理想,本文结合检测模型特点,改进Pearson相似度计算方法。其思想是,降低共同评分项目对用户相似度程度的影响,从而降低填充规模较小的攻击数据与真实用户之间的相似度。实验结果表明该方法对低填充规模攻击有较好的抗攻击性。     

An Accurate and Scalable Collaborative Recommender

We present a collaborative recommender that uses a user-based model to predict user ratings for specified items. The model comprises summary rating information derived from a hierarchical clustering of the users. We compare our algorithm with several others. We show that its accuracy is good and its coverage is maximal. We also show that the algorithm is very efficient: predictions can be made in time that grows independently of the number of ratings and items and only logarithmically in the number of users.     

推荐系统中典型用户群组的发现和应用

推荐系统是解决用户的个性化信息需求的一种有效工具。但随着推荐系统用户规模的扩大,需要合理地从海量用户中筛选出用户子集,并进行持续和深入的分析以改进推荐系统。因此,文中首先提出典型用户群组的概念,以期发现推荐系统中的典型用户子集,从而可正确地反映全体用户的兴趣偏好。随后提出一种典型用户群组的发现算法,通过比较候选新增典型用户对典型用户群组的贡献度,逐一扩大典型用户群组规模,最终达到较高的推荐项目覆盖率和评分准确度。最后在典型用户群组中寻找用户的最近邻,实现一种改进的协同过滤推荐算法。通过在真实数据集上的实验结果表明,与其他用户群组发现算法以及经典推荐算法相比,验证典型用户群组不仅具有较好的代表性,也能够获得更好的推荐效果。     

HU-FCF: A hybrid user-based fuzzy collaborative filtering method in Recommender Systems

Recommender Systems (RS) have been being captured a great attraction of researchers by their applications in various interdisciplinary fields. Fuzzy Recommender Systems (FRS) is an extension of RS with the fuzzy similarity being calculated based on the users’ demographic data instead of the hard user-based degree. Based upon the observations that the FRS researches did not offer a mathematical definition of FRS accompanied with its algebraic operations and properties, and the fuzzy similarity degree is not enough to express accurately the analogousness between users, in this paper we will present a systematic mathematical definition of FRS including theoretical analyses of algebraic operations and properties and propose a novel hybrid user-based fuzzy collaborative filtering method that integrates the fuzzy similarity degrees between users based on the demographic data with the hard user-based degrees calculated from the rating histories into the final similarity degrees in order to obtain high accuracy of prediction. Experimental results on some benchmark datasets show that the proposed method obtains better accuracy than other relevant methods. Lastly, an application for the football results prediction is given to illustrate the uses of the proposed method.     

基于检测响应的安全协同推荐系统研究

协同推荐系统广泛地应用于电子商务和信息访问系统,为新用户提供个性化的产品建议。然而,协同推荐系统存在着严重的安全隐患,使得恶意用户能够注入伪造的描述文件,影响或破坏提供给其他用户的推荐建议。本文探讨了检测响应描述文件注入攻击的方法,改进了协同过滤推荐算法,设计了基于检测响应方法的安全协同推荐系统框架。     

Recommender Systems for Learning: Building User and Expert Models through Long-Term Observation of Application Use

Information technology has recently become the medium in which much professional office work is performed. This change offers an unprecedented opportunity to observe and record exactly how that work is performed. We describe our observation and logging processes and present an overview of the results of our long-term observations of a number of users of one desktop application. We then present our method of providing individualized instruction to each user by employing a new kind of user model and a new kind of expert model. The user model is based on observing the individual's behavior in a natural environment, while the expert model is based on pooling the knowledge of numerous individuals. Individualized instructional topics are selected by comparing an individual's knowledge to the pooled knowledge of her peers.     

Guest Editors's Introduction: Multimedia Systems,Mobile Computing and Global Information Sharing

Multimedia Tools and Applications -     

Personality,User Preferences and Behavior in Recommender systems

This paper reports on a study of 1840 users of the MovieLens recommender system with identified Big-5 personality types. Based on prior literature that suggests that personality type is a stable predictor of user preferences and behavior, we examine factors of user retention and engagement, content preferences, and rating patterns to identify recommender-system related behaviors and preferences that correlate with user personality. We find that personality traits correlate significantly with behaviors and preferences such as newcomer retention, intensity of engagement, activity types, item categories, consumption versus contribution, and rating patterns.     

A Multidimensional Paper Recommender: Experiments and Evaluations

Paper recommender systems in the e-learning domain must consider pedagogical factors, such as a paper's overall popularity and learner background knowledge — factors that are less important in commercial book or movie recommender systems. This article reports evaluations of a 6D paper recommender. Experimental results from a human subject study of learner preferences suggest that pedagogical factors help to overcome a serious cold-start problem (not having enough papers or learners to start the recommender system) and help the system more appropriately support users as they learn.     

George Graham,Philosophy of Mind: An Introduction

Minds and Machines -