基于Apriori算法的协同过滤算法改进研究

针对协同过滤算法推荐结果存在受噪音数据影响严重的问题,提出了一种基于用户项目间的关联规则集的协同过滤算法.利用经典的Apriori算法进行频繁项集合关联规则集的挖掘,利用挖掘的关联规则集进行用户间的相似度计算,相比于pearson相似等方法,基于关联规则集相似可以提高改进算法对噪音数据的抵抗力,最后进行最近邻居集计算并产生更适合用户的推荐结果.改进算法和传统算法在MovieLens数据集上的实验表明,基于Apriori算法的协同过滤算法较传统算法进一步提高了推荐准度和覆盖率.     

协同过滤算法在推荐系统中的应用

介绍了协同过滤算法,并对算法进行了改进,解决了用户稀疏的情况下传统算法的不足,同时通过引入评分阈值,显著提高了个性化协同过滤算法的推荐精度。     

改进的协同过滤推荐算法

传统的选修课系统存在结构性的不足和缺憾,为了避免高校学生盲目的选择选修课程,本文利用改进的协同过滤算法对高校学生进行个性化的选课推荐.本文首先介绍了两种推荐算法,并着重介绍基于协同过滤的推荐算法,并分析了两种算法的优缺点,最后针对协同过滤算法的数据稀疏性问题,提出了一种改进的协同过滤算法,即在协同过滤中加入基于内容的因素来解决这个问题.这种改进的协同过滤算法避免了传统协同过滤算法中存在的数据稀疏问题,以学生为本推荐适合学生的课程,满足学生学习的个性化要求.     

协同过滤算法中推荐集选取方法的研究

针对传统的基于余弦相似性的协同过滤算法中推荐集选取方法进行了改进,设计了一种新的评分方式预测用户对未评价项目的评分,从而增强了推荐的合理性。实验结果表明,该算法同传统协同过滤算法相比能显著提高推荐精度。     

一种改进的均方差协同过滤算法

由于传统基于均方差的协同过滤算法(MSD)计算相似性时仅考虑评分向量间均方差值，导致其推荐性能不理想，针对这个问题，提出融合评分向量间余弦值和均方差值的改进均方差协同过滤算法(Improved MSD， IMSD)。通过在2个Movielens数据集上进行实验表明，IMSD算法较MSD算法的推荐准确度有所提高。更为重要的是，将IMSD算法进行推广应用，也能够取得较好的效果。本文将其应用于改进另外2种算法,即JAC_MSD和AC_MSD算法,并提出了2种相应的JAC_IMSD和AC_IMSD算法，发现算法的推荐准确度都有所提高。在所研究的几种算法中，AC_IMSD算法推荐准确度最优。     

基于心理学模型的协同过滤推荐方法

提出一种改进协同过滤推荐的方法。该方法根据心理学中的态度行为关系理论建立用户浏览购买模型,通过分析用户浏览信息,预测用户对项的评分,根据预测的评分,运用协同过滤推荐算法为用户做出推荐。实验验证了用户浏览购买模型的有效性。与传统协同过滤方法进行对比的结果表明,该方法可以有效地改进协同过滤算法的推荐结果。     

基于协同过滤的数字图书馆推荐系统研究

在海量的数字图书馆中,准确迅速地找到符合自身需要的图书是需要解决的主要问题。通过阐述传统的协同过滤算法,分析其特点以及存在的不足,并基于此提出一种改进的协同过滤算法,建立了推荐系统模型并应用到数字图书馆中。     

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。     

结合置信度和SVD的协同过滤算法

为了解决基于传统模型的协同过滤算法的数据稀疏性与冷启动问题,引入置信度参数,并结合隐式反馈信息,提出了两种基于奇异值分解(SVD)的协同过滤算法,CSVD和NCSVD.CSVD算法在基于偏置的矩阵分解模型上引入了置信度参数,以改进模型偏置项没有针对物品规模根据每个评分调整偏置权重的问题,NCSVD在此基础上引入隐式反馈信息,改善了冷启动问题,在真实数据集上的实验证明表明,其能有效提高SVD系列算法的推荐精度.     

基于混沌粒子群聚类优化的协同过滤推荐

为解决复杂的网络信息无法对用户进行精准推荐的情况,改进传统协同过滤算法,将混沌粒子群算法与协同过滤算法融合使用.在传统粒子群算法中加入混沌扰动并随着迭代调整惯性权重,对用户进行聚类优化.获取目标用户之后,通过判断目标用户属于哪个聚类,在该聚类内部进行协同过滤计算.通过与其它算法之间的对比实验,验证了基于混沌粒子群聚类优化的协同过滤推荐算法相较其它算法具有更低的平均绝对误差和更高的准确率.     

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.     

适应用户兴趣变化的协同过滤推荐算法

协同过滤算法是至今为止最成功的个性化推荐技术之一,被应用到很多领域中 .但传统协同过滤算法不能及时反映用户的兴趣变化 .针对这个问题,提出两种改进度量:基于时间的数据权重和基于资源相似度的数据权重,在此基础上将它们有机结合,并将这两种权重引入基于资源的协同过滤算法的生成推荐过程中 .实验表明,改进后的算法比传统协同过滤算法在推荐准确度上有明显提高 .     

Usage derived recommendations for a video digital library

We describe a minimalist methodology to develop usage-based recommender systems for multimedia digital libraries. A prototype recommender system based on this strategy was implemented for the Open Video Project, a digital library of videos that are freely available for download. Sequential patterns of video retrievals are extracted from the project's web download logs and analyzed to generate a network of video relationships. A spreading activation algorithm locates video recommendations by searching for associative paths connecting query-related videos. We evaluate the performance of the resulting system relative to an item-based collaborative filtering technique operating on user profiles extracted from the same log data.     

An algorithm for efficient privacy-preserving item-based collaborative filtering

Collaborative filtering (CF) methods are widely adopted by existing recommender systems, which can analyze and predict user “ratings” or “preferences” of newly generated items based on user historical behaviors. However, privacy issue arises in this process as sensitive user private data are collected by the recommender server. Recently proposed privacy-preserving collaborative filtering (PPCF) methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in real online services. In this paper, an efficient privacy-preserving item-based collaborative filtering algorithm is proposed, which can protect user privacy during online recommendation process without compromising recommendation accuracy and efficiency. The proposed method is evaluated using the Netflix Prize dataset. Experimental results demonstrate that the proposed method outperforms a randomized perturbation based PPCF solution and a homomorphic encryption based PPCF solution by over 14X and 386X, respectively, in recommendation efficiency while achieving similar or even better recommendation accuracy.     

一种融合项目特征和移动用户信任关系的推荐算法

协同过滤推荐系统中普遍存在评分数据稀疏问题.传统的协同过滤推荐系统中的余弦、Pearson 等方法都是基于共同评分项目来计算用户间的相似度;而在稀疏的评分数据中,用户间共同评分的项目所占比重较小,不能准确地找到偏好相似的用户,从而影响协同过滤推荐的准确度.为了改变基于共同评分项目的用户相似度计算,使用推土机距离（earth mover's distance,简称EMD）实现跨项目的移动用户相似度计算,提出了一种融合项目特征和移动用户信任关系的协同过滤推荐算法.实验结果表明：与余弦、Pearson 方法相比,融合项目特征的用户相似度计算方法能够缓解评分数据稀疏对协同过滤算法的影响.所提出的推荐算法能够提高移动推荐的准确度.     

一种结合评分重合度的协同推荐算法

协同推荐是信息个性化服务中广泛应用的推荐算法,协同推荐算法以宿主系统所观测到的用户评分作为实现推荐的数据依据。用户评分矩阵的稀疏性问题对协同推荐的各工作过程可产生直接或间接的影响,导致推荐服务的准确性下降。通过对稀疏性问题影响推荐系统方式的分析发现,一般协同推荐方法的项目相似度计算只注重项目在评分数值上的相关性,而忽视了项目之间评分的重合度对提高推荐质量所起的重要作用。通过将评分重合度融入到相似度计算中,提出了一种结合评分重合度的改进协同推荐算法,并在稀疏评分环境下将其与已有协同推荐算法进行了对比实验与分析,实验结果验证了所提算法在提高预测准确性上的有效性。     

结合类别偏好信息的Item-based协同过滤算法

传统的基于项目的协同过滤算法离线计算项目相似性,提高了在线推荐速度.但该算法仍然不能解决数据稀疏性所带来的问题,计算出的项目相似性准确度较差,影响了推荐质量.针对这一问题,提出了一种结合类别偏好信息的协同过滤算法,首先为目标项目找出一组类别偏好相似的候选邻居,候选邻居与目标项目性质相近,共同评分较多;在候选邻居中搜寻最近邻,排除了与目标项目共同评分较少项目的干扰,从整体上提高了最近邻搜寻的准确性.实验结果表明,新算法的推荐质量较传统的基于项目的协同过滤算法有显著提高.     

Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce

Based on the introduction to the user-based and item-based collaborative filtering algorithms, the problems related to the two algorithms are analyzed, and a new entropy-based recommendation algorithm is proposed. Aiming at the drawbacks of traditional similarity measurement methods, we put forward an improved similarity measurement method. The entropy-based collaborative filtering algorithm contributes to solving the cold-start problem and discovering users’ hidden interests. Using the data selected from Movielens and Book-Crossing datasets and MAE accuracy metric, three different collaborative filtering recommendation algorithms are compared through experiments. The experimental scheme and results are discussed in detail. The results show that the entropy-based algorithm provides better recommendation quality than user-based algorithm and achieves recommendation accuracy comparable to the item-based algorithm. At last, a solution to B2B e-commerce recommendation applications based on Web services technology is proposed, which adopts entropy-based collaborative filtering recommendation algorithm.     

Identifying the effects of SVD and demographic data use on generalized collaborative filtering

The purpose of this paper is to examine how singular value decomposition (SVD) and demographic information can improve the performance of plain collaborative filtering (CF) algorithms. After a brief introduction to SVD, where the method is explained and some of its applications in recommender systems are detailed, we focus on the proposed technique. Our approach applies SVD in different stages of an algorithm, which can be described as CF enhanced by demographic data. The results of a rather long series of experiments, where the proposed algorithm is successfully blended with user- and item-based CF, show that the combined utilization of SVD with demographic data is promising, since it does not only tackle some of the recorded problems of recommender systems, but also assists in increasing the accuracy of systems employing it.     

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.