基于增量协同过滤和潜在语义分析的混合推荐算法

为了解决传统新闻推荐系统定期更新推荐算法不能根据用户喜好的变化进而动态地调整推荐列表的问题,提出了一种混合推荐算法(IULSACF)。该算法包含了2个关键部分:基于项目的增量更新协同过滤算法和基于关键词频率的潜在语义分析算法。该混合推荐算法在基于项目的增量更新协同过滤模块中,通过对项目相似度列表增量更新来动态地调整推荐列表,并结合潜在语义分析算法来确保所推荐文章的相关性。实验结果表明,所提出的IULSACF算法在各项评价指标上均优于传统的推荐方法。     

Userrank for item-based collaborative filtering recommendation

With the recent explosive growth of the Web, recommendation systems have been widely accepted by users. Item-based Collaborative Filtering (CF) is one of the most popular approaches for determining recommendations. A common problem of current item-based CF approaches is that all users have the same weight when computing the item relationships. To improve the quality of recommendations, we incorporate the weight of a user, userrank, into the computation of item similarities and differentials. In this paper, a data model for userrank calculations, a PageRank-based user ranking approach, and a userrank-based item similarities/differentials computing approach are proposed. Finally, the userrank-based approaches improve the recommendation results of the typical Adjusted Cosine and Slope One item-based CF approaches.     

Knowledge distillation meets recommendation: collaborative distillation for top-N recommendation

Knowledge and Information Systems - Knowledge distillation (KD) is a successful method for transferring knowledge from one model (i.e., teacher model) to another model (i.e., student model)....     

Hybrid recommendation approaches for multi-criteria collaborative filtering

Recommender systems are software tools and techniques for suggesting items in an automated fashion to users tailored their preferences. Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a user’s needs from the neighborhoods of like-minded people, are becoming increasingly popular as ways to overcome the information overload. The multi-criteria based CF presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects and several methods have been proposed for improving the accuracy of these systems. However, the problem of multi-criteria recommendations with a single and overall rating is still considered an optimization problem. In addition, increasing the accuracy in predicting the appropriate items tailored to the users’ preferences is on of the main challenges in these systems. Hence, in this research new recommendation methods using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Self-Organizing Map (SOM) clustering are proposed to improve predictive accuracy of criteria CF. In this research, SOM enables us to generate high quality clusters of dataset and ANFIS is used for discovering knowledge (fuzzy rules) from users’ ratings in multi-criteria dataset, generating appropriate membership functions (MFs), overall rating prediction and input selection. Using exhaustive search method for input selection, the effective inputs are determined to build the ANFIS models in all generated clusters. Furthermore, new fuzzy-based algorithms, Weighted Fuzzy MC-CF (WFuMC-CF), Fuzzy Euclidean MC-CF (FuEucMC-CF) and Fuzzy Average MC-CF (FuAvgMC-CF), are presented for prediction task in multi-criteria CF. FuEucMC-CF and FuAvgMC-CF algorithms uses the fuzzy-based Euclidian distance and fuzzy-based average similarity, respectively, the WFuMC-CF algorithm uses fuzzy-based user- and item-based prediction in a weighted approach. Experimental results on real-world dataset demonstrate that the proposed hybrid methods remarkably improve the accuracy of multi-criteria CF in relation to the previous methods based on multi-criteria ratings.     

基于协同过滤推荐的电价计划智能推荐算法

在智慧电网中,电力公司可以主动推荐定制的售电方案给潜在用户,但现有的推荐算法存在着精确度不高、方案不合理等缺点.为解决以上问题,基于协同过滤策略,开发一种电力计划推荐方案.通过提供一些容易获得的家电产品数据,对居民用户进行不同方案的预测评级,为用户选择合适的方案和合理的电价.在实验阶段,通过不同的数值实验评价该方法的性能,实验结果表明,EPR算法在推荐结果的准确性上优于其它策略.     

融入时间的兴趣点协同推荐算法

兴趣点（POI）推荐可以帮助用户发现其没有访问过但可能感兴趣的地点,是重要的基于位置的服务之一。时间在POI推荐中是一个重要因素,而现有POI推荐模型并没有较好地考虑时间因素,因此通过考虑时间因素来提出融入时间的POI协同推荐（TUCF）算法,从而提高POI推荐的效果。首先,分析基于位置的社交网络（LBSN）的用户签到数据,以探索用户签到的时间关系;然后,利用时间关系对用户签到数据进行平滑处理,以融入时间因素并缓解数据稀疏性;最后,根据基于用户的协同过滤方法,在不同时间推荐不同POI给用户。在真实签到数据集上的实验结果表明,与基于用户的协同过滤（U）算法相比,TUCF算法的精确率和召回率分别提高了63%和69%;与具有平滑增强时间偏好的协同过滤（UTE）算法相比,TUCF算法的精确率和召回率分别提高了8%和12%;并且TUCF算法的平均绝对误差（MAE）比U算法和UTE算法分别减小了1.4%和0.5%。     

协同过滤推荐算法

针对目前协同过滤推荐精度受损,且出现冷启动的问题,提出一种经过改进的协同过滤推荐算法。其主要思想是针对两种不同情况的目标项目采用不同的相似性计算方法。一种项目为新项目,分别通过信息熵法和项目属性相似性计算项目评分,然后通过平衡因子实现新项目评分的组合;另一种项目为非新项目,通过权重因子动态组合项目的属性相似性和评分相似性,获得最近邻居的评分推荐。实验结果表明,该算法能提高推荐算法的稳定性和精确度,同时解决冷启动问题。     

Hiding collaborative recommendation association rules

The concept of Privacy-Preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. For example, through data mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. There have been two types of privacy concerning data mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For output privacy in hiding association rules, current approaches require hidden rules or patterns to be given in advance [10, 18–21, 24, 27]. This selection of rules would require data mining process to be executed first. Based on the discovered rules and privacy requirements, hidden rules or patterns are then selected manually. However, for some applications, we are interested in hiding certain constrained classes of association rules such as collaborative recommendation association rules [15, 22]. To hide such rules, the pre-process of finding these hidden rules can be integrated into the hiding process as long as the recommended items are given. In this work, we propose two algorithms, DCIS (Decrease Confidence by Increase Support) and DCDS (Decrease Confidence by Decrease Support), to automatically hiding collaborative recommendation association rules without pre-mining and selection of hidden rules. Examples illustrating the proposed algorithms are given. Numerical simulations are performed to show the various effects of the algorithms. Recommendations of appropriate usage of the proposed algorithms based on the characteristics of databases are reported. Leon Wang received his Ph.D. in Applied Mathematics from State University of New York at Stony Brook in 1984. From 1984 to 1987, he was an assistant professor in mathematics at University of New Haven, Connecticut, USA. From 1987 to 1994, he joined New York Institute of Technology as a research associate in the Electromagnetic Lab and assistant/associate professor in the Department of Computer Science. From 1994 to 2001, he joined I-Shou University in Taiwan as associate professor in the Department of Information Management. In 1996, he was the Director of Computing Center. From 1997 to 2000, he was the Chairman of Department of Information Management. In 2001, he was Professor and director of Library, all in I-Shou University. In 2002, he was Associate Professor and Chairman in Information Management at National University of Kaohsiung, Taiwan. In 2003, he rejoined New York Institute of Technology. Dr.Wang has published 33 journal papers, 102 conference papers, and 5 book chapters, in the areas of data mining, machine learning, expert systems, and fuzzy databases, etc. Dr. Wang is a member of IEEE, Chinese Fuzzy System Association Taiwan, Chinese Computer Association, and Chinese Information Management Association. Ayat Jafari received the Ph.D. degree from City University of New York. He has conducted considerable research in the areas of Computer Communication Networks, Local Area Networks, and Computer Network Security, and published many technical articles. His interests and expertise are in the area of Computer Networks, Signal Processing, and Digital Communications. He is currently the Chairman of the Computer Science and Electrical Engineering Department of New York Institute of Technology. Tzung-Pei Hong received his B.S. degree in chemical engineering from National Taiwan University in 1985, and his Ph.D. degree in computer science and information engineering from National Chiao-Tung University in 1992. He was a faculty at the Department of Computer Science in Chung-Hua Polytechnic Institute from 1992 to 1994, and at the Department of Information Management in I-Shou University from 1994 to 2001. He was in charge of the whole computerization and library planning for National University of Kaohsiung in Preparation from 1997 to 2000, and served as the first director of the library and computer center in National University of Kaohsiung from 2000 to 2001 and as the Dean of Academic Affairs from 2003 to 2006. He is currently a professor at the Department of Electrical Engineering and at the Department of Computer Science and Information Engineering. His current research interests include machine learning, data mining, soft computing, management information systems, and www applications. Springer     

基于自注意力的协同演进推荐

为解决时序模型不能有效获取用户和项目交互序列的语义关系,以及因参数共享而导致的信息丢失问题,提出基于自注意力的协同演进推荐模型(BSFRNN).将循环神经网络提取的序列特征和自注意力机制提取的语义特征进行融合表征用户以及项目的短期特征,将矩阵分解描述的长期特征和短期时序特征进行融合,将融合的特征向量通过多层感知机进行预...     

改进的协同过滤推荐算法

协同过滤是迄今为止个性化推荐系统中采用最广泛最成功的推荐技术,但现有方法是将用户不同时间的兴趣等同考虑,时效性不足,而且推荐精度也有待进一步提高。鉴于此提出一种改进的协同过滤算法,针对用户近邻计算和项目评分的预测两个关键步骤,提出基于项目相关性的用户相似性计算方法,以便邻居用户更准确,同时在预测评分的过程中增加时间权限,使得接近采集时间的点击兴趣在推荐过程中具有更大权值。实验结果表明,该算法在提高了推荐精度的同时实现了实时推荐。     

Exploring latent browsing graph for question answering recommendation

In this paper, we develop a framework of Question Answering Pages (referred to as QA pages) recommendation. Our proposed framework consists of the two modules: the off-line module to determine the importance of QA pages and the on-line module for on-line QA page recommendation. In the off-line module, we claim that the importance of QA pages could be discovered from user click streams. If the QA pages are of higher importance, many users will click and spend their time on these QA pages. Moreover, the relevant relationships among QA pages are captured by the browsing behavior on these QA pages. As such, we exploit user click streams to model the browsing behavior among QA pages as QA browsing graph structures. The importance of QA pages is derived from our proposed QA browsing graph structures. However, we observe that the QA browsing graph is sparse and that most of the QA pages do not link to other QA pages. This is referred to as a sparsity problem. To overcome this problem, we utilize the latent browsing relations among QA pages to build a QA Latent Browsing Graph. In light of QA latent browsing graph, the importance score of QA pages (referred to as Latent Browsing Rank) and the relevance score of QA pages (referred to as Latent Browsing Recommendation Rank) are proposed. These scores demonstrate the use of a QA latent browsing graph not only to determine the importance of QA pages but also to recommend QA pages. We conducted extensive empirical experiments on Yahoo! Asia Knowledge Plus to evaluate our proposed framework.     

Bilateral neural embedding for collaborative filtering-based multimedia recommendation

Multimedia Tools and Applications - As one of the most popular and successfully applied recommendation methods, collaborative filtering aims to extract low-dimensional user and item representation...     

面向社会化电子商务的信任感知协同过滤推荐方法

为提高社会化电子商务推荐服务的精确度和有效性,综合考虑交易评价得分、交易次数、交易金额、直接信任、推荐信任等影响社会化电子商务用户信任关系的因素,设计了一种信任感知协同过滤推荐方法.该方法利用置信因子计算用户间的信任关系,采用余弦相关度法计算用户间的相似度,引入调和因子综合用户信任关系和用户相似度对商品预测评分的影响,以平均绝对误差(MAE)、评分覆盖率和用户覆盖率作为评价指标.实验结果表明,与标准协同过滤推荐方法、基于规范矩阵因式分解的推荐方法相比,信任感知协同过滤推荐方法将MAE降低到0.162,并将评分覆盖率和用户覆盖率分别提高到77%和80%,能够解决交易评价较少商品的推荐问题.     

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.     

Identifiability of extended latent class models with individual covariates

Identifiability for a very flexible family of latent class models introduced recently is examined. These models allow for a conditional association between selected pairs of response variables conditionally on the latent and are based on logistic regression models both for the latent weights and for the conditional distributions of the response variables in terms of subject specific covariates. Generalized logits (global or continuation, which are relevant with ordered categorical responses and involve comparisons of cumulated probabilities) may be used as an alternative to the usual logits of type local which are log-linear. A compact matrix formulation for the Jacobian of the parametrization and a simple algorithm for checking local identifiability numerically is described. A few examples involving causal inference are examined.     

基于位置的非对称相似性度量的协同过滤推荐算法

为提升推荐系统的准确率,针对传统协同过滤(CF)推荐算法没有有效使用位置信息的问题,提出了一种基于位置的非对称相似性度量的协同过滤推荐算法(LBASCF)。首先,分别利用用户-商品评分矩阵和用户历史消费位置,计算出用户间的余弦相似性和基于位置的非对称相似性;其次,将余弦相似性与基于位置的相似性融合,得到一个新的非对称用户相似性,融合后的相似性能够同时反映用户在位置上和兴趣上的偏好;最后,根据用户的最近邻居对商品的评分向用户推荐新的商品。用某点评数据集和Foursquare数据集对算法的有效性进行了评估。在某点评数据集实验结果证明,与CF相比,LBASCF的召回率和精确率分别提高了1.64%和0.37%;与位置感知协同过滤推荐系统(LARS)方法比较,LBASCF的召回率和精确率分别提高了1.53%和0.35%。实验结果表明,LBASCF相对于CF和LARS在基于位置服务的应用中能够有效提高系统的推荐质量。     

Neural content-aware collaborative filtering for cold-start music recommendation

Data Mining and Knowledge Discovery - State-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from the...     

Exploit latent Dirichlet allocation for collaborative filtering

Previous work on the one-class collaborative filtering (OCCF) problem can be roughly categorized into pointwise methods, pairwise methods, and content-based methods. A fundamental assumption of these approaches is that all missing values in the user-item rating matrix are considered negative. However, this assumption may not hold because the missing values may contain negative and positive examples. For example, a user who fails to give positive feedback about an item may not necessarily dislike it; he may simply be unfamiliar with it. Meanwhile, content-based methods, e.g. collaborative topic regression (CTR), usually require textual content information of the items, and thus their applicability is largely limited when the text information is not available. In this paper, we propose to apply the latent Dirichlet allocation (LDA) model on OCCF to address the above-mentioned problems. The basic idea of this approach is that items are regarded as words, users are considered as documents, and the user-item feedback matrix constitutes the corpus. Our model drops the strong assumption that missing values are all negative and only utilizes the observed data to predict a user’s interest. Additionally, the proposed model does not need content information of the items. Experimental results indicate that the proposed method outperforms previous methods on various ranking-oriented evaluation metrics. We further combine this method with a matrix factorization-based method to tackle the multi-class collaborative filtering (MCCF) problem, which also achieves better performance on predicting user ratings.     

关联性动态加权的协同过滤推荐

利用传统的协同过滤（CF）算法进行推荐时,由于用户评分矩阵比较稀疏,直接得到的用户或者项目之间的相似度相对而言可信度就比较低。为了解决这个问题,在传统的协同过滤基础上,引入项目与项目之间的关联性,通过在项目的类别标签和二部图的方法之间构建动态权重因子来融合这两种关联,形成非对等关联性关系,并做出用户对项目的评分预测,从而解决评分矩阵过于稀疏的问题。研究结果表明,相比于传统方法中使用对等相似度关系以及固定权值的方法,通过动态权重融合关联性形成非对等的关系的方法,更贴合生活实际,并且有更好的推荐效果。     

安全的半监督方法的协同过滤推荐算法

为解决传统协同过滤算法中用户评分数据稀疏性,忽视物品及用户特征,所带来的推荐质量下降的问题,提出了一种基于安全的、高置信度的半监督方法的协同过滤推荐算法,采用安全的,高置信度的半监督方法S4VM对没有评分的数据进行有效预测,同时考虑用户的行为信息以及物品及用户特征。通过对未评分数据进行预测,能够有效地缓解数据的稀疏性,从而提高寻找最近邻的准确度。实验结果表明,该算法能够有效地提高系统的推荐质量。