一种融合聚类与用户兴趣偏好的协同过滤推荐算法

协同过滤推荐算法可以根据已知用户的偏好预测其可能感兴趣的项目,是现今最为成功、应用最广泛的推荐技术。然而,传统的协同过滤推荐算法受限于数据稀疏性问题,推荐结果较差。目前的协同过滤推荐算法大多只针对用户-项目评分矩阵进行数据分析,忽视了项目属性特征及用户对项目属性特征的偏好。针对上述问题,提出了一种融合聚类和用户兴趣偏好的协同过滤推荐算法。首先根据用户评分矩阵与项目类型信息,构建用户针对项目类型的用户兴趣偏好矩阵;然后利用K-Means算法对项目集进行聚类,并基于用户兴趣偏好矩阵查找待估值项所对应的近邻用户;在此基础上,通过结合项目相似度的加权Slope One算法在每一个项目类簇中对稀疏矩阵进行填充,以缓解数据稀疏性问题;进而基于用户兴趣偏好矩阵对用户进行聚类;最后,面向填充后的评分矩阵,在每一个用户类簇中使用基于用户的协同过滤算法对项目评分进行预测。实验结果表明,所提算法能够有效缓解原始评分矩阵的稀疏性问题,提升算法的推荐质量。     

Distributed collaborative filtering with singular ratings for large scale recommendation

Collaborative filtering (CF) is an effective technique addressing the information overloading problem, where each user is associated with a set of rating scores on a set of items. For a chosen target user, conventional CF algorithms measure similarity between this user and other users by utilizing pairs of rating scores on common rated items, but discarding scores rated by one of them only. We call these comparative scores as dual ratings, while the non-comparative scores as singular ratings. Our experiments show that only about 10% ratings are dual ones that can be used for similarity evaluation, while the other 90% are singular ones. In this paper, we propose SingCF approach, which attempts to incorporate multiple singular ratings, in addition to dual ratings, to implement collaborative filtering, aiming at improving the recommendation accuracy. We first estimate the unrated scores for singular ratings and transform them into dual ones. Then we perform a CF process to discover neighborhood users and make predictions for each target user. Furthermore, we provide a MapReduce-based distributed framework on Hadoop for significant improvement in efficiency. Experiments in comparison with the state-of-the-art methods demonstrate the performance gains of our approaches.     

Applications of wavelet data reduction in a recommender system

Memory-based collaborative filtering (CF) recommender systems have emerged as an effective technique for information filtering. CF recommenders are being widely adopted for e-commerce applications to assist users in finding and selecting items of interest. As a result, the scalability of CF recommenders presents a significant challenge; one that is particularly resilient because the volume of data these systems utilize will continue to increase over time. This paper examines the impact of discrete wavelet transformation (DWT) as an approach to enhance the scalability of memory-based collaborative filtering recommender systems. In particular, a wavelet transformation methodology is proposed and applied to both synthetic and real-world recommender ratings. For experimental purposes, the DWT methodology’s effect on predictive accuracy and calculation speed is evaluated to compare recommendation quality and performance.     

查询推荐技术综述

本文介绍了用户搜索中查询推荐技术的相关概念、研究现状;深入分析了目前常见的推荐算法及推荐系统中的隐私保护问题;最后,归纳了查询推荐技术的研究热点。     

基于人口统计学的改进聚类模型协同过滤算法

针对传统基于用户的协同过滤推荐算法在大数据环境下存在评分高维稀疏性、推荐精度低的问题,提出一种基于人口统计学数据与改进聚类模型相结合的协同过滤推荐算法,以提高推荐系统精度和泛化能力。该方法首先通过用户人口统计学数据属性,结合用户-项目评分矩阵计算各个用户间的相似度;然后对用户、项目进行分层近邻传播聚类,根据用户对项目的评分数据计算用户或项目之间的相似性,产生目标用户或项目的兴趣近邻;最后根据兴趣最近邻进行推荐。对Epinions,MovieLents等数据集进行仿真实验,仿真的结果表明, 与传统的协同过滤算法相比, 提出的算法提高了推荐精度,为传统的协同过滤推荐算法提供了参考。     

Collaborative error-reflected models for cold-start recommender systems

Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users to easily find useful information. One notable challenge in practical CF is the cold start problem, which can be divided into cold start items and cold start users. Traditional CF systems are typically unable to make good quality recommendations in the situation where users and items have few opinions. To address these issues, in this paper, we propose a unique method of building models derived from explicit ratings and we apply the models to CF recommender systems. The proposed method first predicts actual ratings and subsequently identifies prediction errors for each user. From this error information, pre-computed models, collectively called the error-reflected model, are built. We then apply the models to new predictions. Experimental results show that our approach obtains significant improvement in dealing with cold start problems, compared to existing work.     

On the combination of user-based and item-based collaborative filtering

In this paper, we propose two new filtering algorithms which are a combination of user-based and item-based collaborative filtering schemes. The first one, Hybrid-Ib, identifies a reasonably large neighbourhood of similar users and then uses this subset to derive the item-based recommendation model. The second algorithm, Hybrid-CF, starts by locating items similar to the one for which we want a prediction, and then, based on that neighbourhood, it generates its user-based predictions. We start by describing the execution steps of the algorithms and proceed with extended experiments. We conclude that our algorithms are directly comparable to existing filtering approaches, with Hybrid-CF producing favorable or, in the worst case, similar results in all selected evaluation metrics.     

The impact of data obfuscation on the accuracy of collaborative filtering

Collaborative filtering (CF) is a widely-used technique for generating personalized recommendations. CF systems are typically based on a central storage of user profiles, i.e., the ratings given by users to items. Such centralized storage introduces potential privacy breach, since all the user profiles may be accessible by untrusted parties when breaking the access control of the centralized system. Hence, recent studies have focused on enhancing the privacy of CF users by distributing their user profiles across multiple repositories and obfuscating the user profiles to partially hide the actual user ratings. This work combines these two techniques and investigates the unavoidable side effect of data obfuscation: the reduction of the accuracy of the generated CF predictions. The evaluation, which was conducted using three different datasets, shows that considerable parts of the user profiles can be modified without observing a substantial decrease of the CF prediction accuracy. The evaluation also indicates what parts of the user profiles are required for generating accurate CF predictions. In addition, we conducted an exploratory user study that reveals positive attitude of users towards the data obfuscation.     

基于协同过滤的位置感知推荐

不同地区的用户兴趣不同,并且当推荐物品具有位置属性时,用户更加倾向于离自身较近的物品。根据用户和物品的位置信息来捕获用户兴趣能有效地提高个性化推荐精度。为了有效处理用户和物品的位置信息,在推荐系统中引入金字塔模型(PS)来实现用户分区和用户旅行代价的计算,提出了基于金字塔模型的协同过滤算法(PMCF),来生成对用户的Top-N物品推荐。使用MovieLens数据集、Foursquare数据集和Synthetic数据集来分别评估算法的有效性,实验表明,所提出的算法的准确度要高于传统的推荐算法。     

基于重叠度和双重属性的协同过滤推荐算法

协同过滤是现行推荐系统中应用最广泛也是最成功的推荐技术之一,然而传统的协同过滤推荐算法存在着邻居选取片面性和推荐精度低的问题。针对上述问题,提出了一种基于重叠度和双重属性的协同过滤推荐算法。首先基于相似度和重叠度的共同计算结果选取推荐对象集;然后提出了双重属性的概念,分别计算推荐用户的信任度和目标项目的受欢迎度;最后兼顾两个群体,根据用户和项目两方面的评分信息完成对目标用户的推荐。实验结果证明该算法较传统的协同过滤推荐算法在邻居选取和推荐质量方面均有显著的提高。     

基于信任和概率矩阵分解的协同推荐算法研究

为了解决推荐系统的冷启动和数据稀疏性问题,研究人员利用用户之间的信任关系,提出了多种基于信任的协同推荐算法,这些方法提高了推荐覆盖率,然而推荐精确度却有所降低。因此,本文综合考虑用户之间的信任关系和用户的潜在特征,提出了基于信任和概率矩阵分解的协同推荐算法,首先通过融入用户的相似性、影响力、专业性等知识,计算用户之间不对称的信任关系;然后结合概率矩阵分解模型进行评分预测;最后在数据集上进行实验测试评估,实验表明该算法可以有效提高推荐结果的精确度。     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.     

AICF: Attention-based item collaborative filtering

Item-to-item collaborative filtering (short for ICF) has been widely used in ecommerce websites due to his interpretability and simplicity in real-time personalized recommendation. The focus of ICF is to calculate the similarity between items. With the rapid development of machine learning in recent years, it takes similarity model instead of cosine similarity and Pearson coefficient to calculate the similarity between items in recommendation. However, the existing similarity models can not sufficient to express the preferences of users for different items. In this work, we propose a novel attention-based item collaborative filtering model(AICF) which adopts three different attention mechanisms to estimate the weights of historical items that users have interacted with. Compared with the state-of-the-art recommendation models, the AICF model with simple attention mechanism Self-Attention can better estimate the weight of historical items on non-sparse data sets. Due to depth models can model complex connection between items, our model with the more complex Transformer achieves superior recommendation performance on sparse data. Extensive experiments on ML-1M and Pinterest-20 show that the proposed model greatly outperforms other novel models in recommendation accuracy and provides users with personalized recommendation list more in line with their interests.     

Property-based collaborative filtering for health-aware recommender systems

Recommender systems aim at solving the problem of information overload by selecting items (commercial products, educational assets, TV programs, etc.) that match the consumers’ interests and preferences. Recently, there have been approaches to drive the recommendations by the information stored in electronic health records, for which the traditional strategies applied in online shopping, e-learning, entertainment and other areas have several pitfalls. This paper addresses those problems by introducing a new filtering strategy, centered on the properties that characterize the items and the users. Preliminary experiments with real users have proved that this approach outperforms previous ones in terms of consumers’ satisfaction with the recommended items. The benefits are especially apparent among people with specific health concerns.     

基于协同过滤的在线拍卖商品推荐

随着电子商务的发展，基于C2C环境的在线拍卖也迅速发展起来，用户数和拍卖商品数的急剧增加，使得信息过载和如何提高客户忠诚度这一问题凸现出来。而推荐系统则成了解决这一问题的手段之一。但是C2C和B2C环境存在很大的不同，对推荐系统的应用提出了一定的挑战。该文对用户在拍卖网站的行为进行了分析，在此基础上建立了用户的偏好模型，利用协同过滤技术进行拍卖商品的推荐。     

A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches. Cane Wing-ki Leung is a PhD student in the Department of Computing, The Hong Kong Polytechnic University, where she received her BA degree in Computing in 2003. Her research interests include collaborative filtering, data mining and computer-supported collaborative work. Stephen Chi-fai Chan is an Associate Professor and Associate Head of the Department of Computing, The Hong Kong Polytechnic University. Dr. Chan received his PhD from the University of Rochester, USA, worked on computer-aided design at Neo-Visuals, Inc. in Toronto, Canada, and researched in computer-integrated manufacturing at the National Research Council of Canada before joining the Hong Kong Polytechnic University in 1993. He is currently working on the development of collaborative Web-based information systems, with applications in education, electronic commerce, and manufacturing. Fu-lai Chung received his BSc degree from the University of Manitoba, Canada, in 1987, and his MPhil and PhD degrees from the Chinese University of Hong Kong in 1991 and 1995, respectively. He joined the Department of Computing, Hong Kong Polytechnic University in 1994, where he is currently an Associate Professor. He has published widely in the areas of computational intelligence, pattern recognition and recently data mining and multimedia in international journals and conferences and his current research interests include time series data mining, Web data mining, bioinformatics data mining, multimedia content analysis,and new computational intelligence techniques.     

使用二分图网络提高协同推荐的准确性

协同过滤是推荐系统中广泛使用的最成功的推荐技术,但却面临着严峻的稀疏性问题.评分数据稀疏性使得最近邻搜寻不够准确,导致推荐质量较差.使用二分图网络缓解协同过滤推荐系统中的稀疏性问题,即将用户和项目抽象为二分图网络中的节点,重新分配项目资源并计算项目间资源贴近度,据此填充用户未评分项目,将稀疏评分矩阵转化为完全矩阵.采用近邻传播聚类对评分矩阵进行聚类,提高算法的可扩展性.最后提出了两种不同的在线推荐策略:(1)通过加权目标用户所在类的邻居用户评分产生推荐(BNAPC1);(2)通过各个类的总体偏好产生推荐(BNAPC2).在MovieLens和Netflix数据集上进行了实验,结果表明BNAPC1的预测精度优于BNAPC2,且与其他几种常用的推荐算法相比仍具有一定优势.     

Pessimists and optimists: Improving collaborative filtering through sentiment analysis

This work presents a novel application of Sentiment Analysis in Recommender Systems by categorizing users according to the average polarity of their comments. These categories are used as attributes in Collaborative Filtering algorithms. To test this solution a new corpus of opinions on movies obtained from the Internet Movie Database (IMDb) has been generated, so both ratings and comments are available. The experiments stress the informative value of comments. By applying Sentiment Analysis approaches some Collaborative Filtering algorithms can be improved in rating prediction tasks. The results indicate that we obtain a more reliable prediction considering only the opinion text (RMSE of 1.868), than when apply similarities over the entire user community (RMSE of 2.134) and sentiment analysis can be advantageous to recommender systems.     

安全推荐系统中基于信任的检测模型

在网格环境中,推荐系统通过提供高品质的个性化推荐,帮助网格用户选择更好的服务。另外,推荐系统也应用于虚拟机管理平台来评估虚拟机的性能和可靠性。然而,推荐结果对用户偏好信息的敏感性使得推荐系统易受到人为攻击(用户概貌注入攻击或托攻击)。本文中,我们提出并评估了一种新的基于信任的安全检测算法以保护推荐系统抵御用户概貌注入攻击。并且,我们分别在用户级和项目级上讨论了信任检测与RDMA检测的结合。最后,我们通过试验表明这些新的安全检测机制可以取得更好的检测精度。     

Collaborative filtering based on workflow space

The traditional recommender systems are usually oriented to general situations in daily lives (e.g. recommend movies, books, music, news and etc.), but seldom cover the recommendation scenarios for the collaborative team environments. We have done an explorative study on collaborative filtering mechanism for collaborative team environments, which is some kind of multi-dimensional recommender systems problem with consideration of workflow context. This paper proposed 3-dimensional workflow space model, and investigated the new similarities measure between members in workflow space. Then, the new similarities measure is utilized into collaborative filtering for recommender systems in collaborative team environments. At last, the efficiency and usability of the proposed method are validated by experiments referring to a real-world collaborative team of a manufacturing enterprise.