基于多源信息相似度的微博用户推荐算法

针对传统的协同过滤（CF）推荐算法中存在的数据稀疏性和推荐准确率不高的问题,提出了基于多源信息相似度的微博用户推荐算法（MISUR）。首先,根据微博用户的标签信息运用K最近邻（KNN）算法对用户进行分类;然后,对得到的每个类中的用户分别计算其多源信息（微博内容、交互关系和社交信息）的相似度;其次,引入时间权重和丰富度权重计算多源信息的总相似度,并根据其大小进行TOP-N用户推荐;最后,在并行计算框架Spark上进行实验。实验结果表明,MISUR算法与CF算法和基于多社交行为的微博好友推荐算法（MBFR）相比,在准确率、召回率和效率方面都有较大幅度的提升,说明了MISUR算法的有效性。     

基于用户网络嵌入的民宿房源推荐方法

随着民宿行业的迅速发展,在线民宿订房系统开始流行起来。让用户在海量房源信息中快速找到所需房源是订房系统中待解决的问题。针对房源推荐中用户冷启动与数据稀疏性的问题,提出基于网络嵌入法的房源个性化推荐（UNER）方法。首先通过用户在系统中的历史行为数据及标签信息构建两类用户网络;然后基于网络嵌入法将网络映射至低维向量空间中,得到用户节点的向量表示并通过用户向量计算用户相似度矩阵;最后依据该矩阵为用户进行房源推荐。实验数据来源于贵州"水东乡舍"民宿订房系统。实验结果表明,相对于基于用户的协同过滤算法,所提方法的综合评价指标（F1）提升了20个百分点,平均正确率（MAP）提升11个百分点,体现出该方法的优越性。     

A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for products or services during a live interaction. These systems, especially collaborative filtering based on user, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the kinds of commodity to Web sites in recent years poses some key challenges for recommender systems. One of these challenges is ability of recommender systems to be adaptive to environment where users have many completely different interests or items have completely different content (We called it as Multiple interests and Multiple-content problem). Unfortunately, the traditional collaborative filtering systems can not make accurate recommendation for the two cases because the predicted item for active user is not consist with the common interests of his neighbor users. To address this issue we have explored a hybrid collaborative filtering method, collaborative filtering based on item and user techniques, by combining collaborative filtering based on item and collaborative filtering based on user together. Collaborative filtering based on item and user analyze the user-item matrix to identify similarity of target item to other items, generate similar items of target item, and determine neighbor users of active user for target item according to similarity of other users to active user based on similar items of target item.In this paper we firstly analyze limitation of collaborative filtering based on user and collaborative filtering based on item algorithms respectively and emphatically make explain why collaborative filtering based on user is not adaptive to Multiple-interests and Multiple-content recommendation. Based on analysis, we present collaborative filtering based on item and user for Multiple-interests and Multiple-content recommendation. Finally, we experimentally evaluate the results and compare them with collaborative filtering based on user and collaborative filtering based on item, respectively. The experiments suggest that collaborative filtering based on item and user provide better recommendation quality than collaborative filtering based on user and collaborative filtering based on item dramatically.     

Exploiting rich user information for one-class collaborative filtering

One-class collaborative filtering (OCCF) is an emerging setup in collaborative filtering in which only positive examples or implicit feedback can be observed. Compared with the traditional collaborative filtering setting where the data have ratings, OCCF is more realistic in many scenarios when no ratings are available. In this paper, we propose to improve OCCF accuracy by exploiting the rich user information that is often naturally available in community-based interactive information systems, including a user’s search query history, and purchasing and browsing activities. We propose two major strategies to incorporate such user information into the OCCF models: One is to linearly combine scores from different sources, and the other is to embed user information into collaborative filtering. Furthermore, we employ the MapReduce framework for similarity computation over millions of users and items. Experimental results on two large-scale retail datasets from a major e-commerce company show that the proposed methods are effective and can improve the performance of the OCCF over baseline methods through leveraging rich user information.     

Improving sparsity and new user problems in collaborative filtering by clustering the personality factors

In collaborative filtering recommender systems, items recommended to an active user are selected based on the interests of users similar to him/her. Collaborative filtering systems suffer from the ‘sparsity’ and ‘new user’ problems. The former refers to the insufficiency of data about users’ preferences and the latter addresses the lack of enough information about the new-coming user. Clustering users is an effective way to improve the performance of collaborative filtering systems in facing the aforementioned problems. In previous studies, users were clustered based on characteristics such as ratings given by them as well as their age, gender, occupation, and geographical location. On the other hand, studies show that there is a significant relationship between users’ personality traits and their interests. To alleviate the sparsity and new user problems, this paper presents a new collaborative filtering system in which users are clustered based on their ‘personality traits’. In the proposed method, the personality of each user is described according to the big-5 personality model and users with similar personality are placed in the same cluster using K-means algorithm. The unknown ratings of the sparse user-item matrix are then estimated based on the clustered users, and recommendations are found for a new user according to a user-based approach which relays on the interests of the users with similar personality to him/her. In addition, for an existing user in the system, recommendations are offered in an item-based approach in which the similarity of items is estimated based on the ratings of users similar to him/her in personality. The proposed method is compared to some former collaborative filtering systems. The results demonstrate that in facing the data sparsity and new user problems, this method reduces the mean absolute error and improves the precision of the recommendations.     

一种结合用户可信度与相似度的鲁棒性推荐算法

协同过滤推荐系统面临着托攻击的安全威胁。研究抵御托攻击的鲁棒性推荐算法已成为一个迫切的课题。传统的鲁棒性推荐算法在算法稳定性与推荐准确度之间难以权衡。针对该问题,首先定义一种用户可信度指标,其次改进传统的相似度计算方法,通过结合用户可信度与改进的相似度,滤除攻击概貌,为目标用户作出推荐。实验表明,与传统算法相比,本文算法具备更强的稳定性,同时保持了良好的推荐准确度。     

Collaborative user modeling for enhanced content filtering in recommender systems

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.     

Personalized recommendation based on review topics

The traditional collaborative filtering algorithm is a successful recommendation technology. The core idea of this algorithm is to calculate user or item similarity based on user ratings and then to predict ratings and recommend items based on similar users’ or similar items’ ratings. However, real applications face a problem of data sparsity because most users provide only a few ratings, such that the traditional collaborative filtering algorithm cannot produce satisfactory results. This paper proposes a new topic model-based similarity and two recommendation algorithms: user-based collaborative filtering with topic model algorithm (UCFTM, in this paper) and item-based collaborative filtering with topic model algorithm (ICFTM, in this paper). Each review is processed using the topic model to generate review topic allocations representing a user’s preference for a product’s different features. The UCFTM algorithm aggregates all topic allocations of reviews by the same user and calculates the user most valued features representing product features that the user most values. User similarity is calculated based on user most valued features, whereas ratings are predicted from similar users’ ratings. The ICFTM algorithm aggregates all topic allocations of reviews for the same product, and item most valued features representing the most valued features of the product are calculated. Item similarity is calculated based on item most valued features, whereas ratings are predicted from similar items’ ratings. Experiments on six data sets from Amazon indicate that when most users give only one review and one rating, our algorithms exhibit better prediction accuracy than other traditional collaborative filtering and state-of-the-art topic model-based recommendation algorithms.     

基于元相似度的推荐算法*

提出了一个新的相似度概念——元相似度,并在此基础上对标准的协同过滤算法进行了改进.元相似度即相似度的相似度,与相似度相比元相似度是基于相似度矩阵而不是相关矩阵计算得出的.即使是在相关矩阵中未购买过任何相同商品的两个用户也可以用元相似度反映他们之间的相似关系,这样在一定程度上解决了冷启动和矩阵稀疏性问题.综合考虑元相似度...     

基于社交和地理信息的兴趣点推荐

目前基于用户的协同过滤兴趣点推荐模型认为两个用户之间对彼此的影响是相同的；同时，在计算社交用户相似度时仅仅考虑了用户的朋友集合，未考虑用户住所的地理信息。针对上述问题，提出了一种融合用户、社会和地理信息的兴趣点推荐（Fuse Users、Social and Geographic，FUSG）模型。将非对称用户影响和PageRank算法融入到基于用户的协同过滤算法中，挖掘用户偏好对兴趣点推荐系统的影响；结合社交用户之间的居住距离和用户的共同好友计算用户之间的相似度；利用地理信息挖掘用户签到的地理特征；将改进的协同过滤算法、社交信息与地理信息融合成FUSG模型，进行兴趣点推荐。在真实的数据集上的实验结果表明，FUSG模型不仅能够缓解冷启动问题，且与其他模型相比具有更高的推荐结果。     

基于用户相似度和信任度的药品推荐算法

为了解决协同过滤算法推荐精度低的问题,提出基于用户相似度和信任度的药品推荐算法。该方法通过离线使用DBSCAN算法对药品进行聚类来降低时间复杂度。引入共同评分药品阈值使用户相似度计算更准确,同时设置相似度阈值来限定相似性邻居的选取以克服KNN算法选取邻居的缺陷。根据用户的推荐可信度和评分可信度建立信任计算模型,计算基于相似邻居集的可信邻居集。通过两次邻居选择策略为目标用户产生药品推荐。仿真结果表明,该算法与其他算法相比在平均绝对误差、准确率和召回率上有更好的性能,提高了系统推荐精度。     

面向用户群体的Web服务推荐

针对Web服务推荐中服务用户调用Web服务的服务质量数据稀疏性导致的低推荐质量问题,提出了一种面向用户群体并基于协同过滤的Web服务推荐算法(WRUG)。首先,为每个服务用户根据用户相似性矩阵构建其个性化的相似用户群体;其次,以相似用户群体中心点代替群体从而计算用户群体相似性矩阵;最后,构造面向群体的Web服务推荐公式并为目标用户预测缺失的Web服务质量。通过对197万条真实Web服务质量调用记录的数据集进行对比实验,与传统基于协同过滤的推荐算法(TCF)和基于用户群体影响的协同过滤推荐算法(CFBUGI)相比,WRUG的平均绝对误差下降幅度分别为28.9%和4.57%;并且WRUG的覆盖率上升幅度分别为110%和22.5%。实验结果表明,在相同实验条件下WRUG不仅能提高Web服务推荐系统的预测准确性,而且能显著地提高其有效预测服务质量的百分比。     

Learning bidirectional asymmetric similarity for collaborative filtering via matrix factorization

Memory-based collaborative filtering (CF) aims at predicting the rating of a certain item for a particular user based on the previous ratings from similar users and/or similar items. Previous studies in finding similar users and items have several drawbacks. First, they are based on user-defined similarity measurements, such as Pearson Correlation Coefficient (PCC) or Vector Space Similarity (VSS), which are, for the most part, not adaptive and optimized for specific applications and data. Second, these similarity measures are restricted to symmetric ones such that the similarity between A and B is the same as that for B and A, although symmetry may not always hold in many real world applications. Third, they typically treat the similarity functions between users and functions between items separately. However, in reality, the similarities between users and between items are inter-related. In this paper, we propose a novel unified model for users and items, known as Similarity Learning based Collaborative Filtering (SLCF) , based on a novel adaptive bidirectional asymmetric similarity measurement. Our proposed model automatically learns asymmetric similarities between users and items at the same time through matrix factorization. Theoretical analysis shows that our model is a novel generalization of singular value decomposition (SVD). We show that, once the similarity relation is learned, it can be used flexibly in many ways for rating prediction. To take full advantage of the model, we propose several strategies to make the best use of the proposed similarity function for rating prediction. The similarity can be used either to improve the memory-based approaches or directly in a model based CF approaches. In addition, we also propose an online version of the rating prediction method to incorporate new users and new items. We evaluate SLCF using three benchmark datasets, including MovieLens, EachMovie and Netflix, through which we show that our methods can outperform many state-of-the-art baselines.     

混合协同过滤算法中用户冷启动问题的研究

在推荐系统中,用户冷启动问题是传统协同过滤推荐系统中一直存在的问题。针对这个问题,在传统协同过滤算法的基础上,提出一种新的解决用户冷启动问题的混合协同过滤算法,该算法在计算用户相似性时引入用户信任机制和人口统计学信息,综合考虑用户的属性相似性和信任相似性。同时,算法还在用户近邻的选取上做了一些改进。实验表明该算法有效缓解了传统协同过滤推荐系统中的用户冷启动问题。     

An effective collaborative filtering algorithm based on user preference clustering

Collaborative filtering is one of widely used recommendation approaches to make recommendation services for users. The core of this approach is to improve capability for finding accurate and reliable neighbors of active users. However, collected data is extremely sparse in the user-item rating matrix, meanwhile many existing similarity measure methods using in collaborative filtering are not much effective, which result in the poor performance. In this paper, a novel effective collaborative filtering algorithm based on user preference clustering is proposed to reduce the impact of the data sparsity. First, user groups are introduced to distinguish users with different preferences. Then, considering the preference of the active user, we obtain the nearest neighbor set from corresponding user group/user groups. Besides, a new similarity measure method is proposed to preferably calculate the similarity between users, which considers user preference in the local and global perspectives, respectively. Finally, experimental results on two benchmark data sets show that the proposed algorithm is effective to improve the performance of recommender systems.     

基于项目分类和用户群体兴趣的协同过滤算法

由于用户评分数据在极端稀疏的情况下会导致传统协同过滤算法的推荐质量下降,针对该问题,提出一种基于项目分类和用户群体兴趣的协同过滤算法。该算法根据项目类别信息对项目进行分类,相同分类的项目具有较高的相似性;利用评分数据计算各个项目分类上的用户相似性矩阵,并计算用户群体在各个分类上的兴趣,通过二者构造加权的用户相似性矩阵;利用用户加权相似性矩阵寻找用户的最近邻以获得最佳的推荐效果。实验结果表明,该算法能有效提高推荐质量。     

基于改进型启发式相似度模型的协同过滤推荐方法

为提高协同过滤推荐方法的准确性和有效性,提出一种基于改进型启发式相似度模型的协同过滤推荐方法PSJ。该方法考虑了用户评分差值、用户全局评分偏好和用户共同评分物品数三个因素。PSJ方法的Proximity因子使用指数函数反映用户评分差值对用户相似度的影响,这样也可避免零除问题;将NHSM方法中的Significance因子和URP因子合并成PSJ方法的Significance因子,这使得PSJ方法的计算复杂度低于NHSM方法;而且为了提高在数据稀疏情况下的推荐效果,PSJ方法同时考虑了用户间的评分差值和用户全局评分两个因素。实验采用Top-k推荐中的查准率和查全率作为衡量标准。实验结果表明,当推荐物品数大于20时,与NHSM、杰卡尔德算法、自适应余弦相似度（ACOS）算法、杰卡尔德均方差（JMSD）算法和皮尔逊相关系数算法（SPCC）相比,PSJ方法的查准率与查全率均有提升。     

融合正态分布函数相似度的协同过滤算法

传统协同过滤推荐算法的相似度量方法仅考虑用户间共同评分,忽略了用户间潜在共同评分项等信息量对推荐结果的影响。针对上述问题,设计了一种正态分布函数相似度量模型,此模型考虑了用户间的共同评分、共同评分项目数、以及用户的评分值,据此提出了融合正态分布函数相似度的协同过滤算法,该算法通过综合多种评分因素利用正态分布函数和修正的余弦相似度共同度量用户间的相似关系。实验结果表明,在两种数据集上与几种不同的推荐算法相比,该算法的相似度量方法提高了目标用户查找邻近用户集合的准确率,提高了系统的推荐质量。     

Constructing a user similarity network to remove adverse influence of popular objects for personalized recommendation

Nowadays, personalized recommender systems have become more and more indispensable in a wide variety of commercial applications due to the vast amount of overloaded information accompanying the explosive growth of the internet. Based on the assumption that users sharing similar preferences in history would also have similar interests in the future, user-based collaborative filtering algorithms have demonstrated remarkable successes and become one of the most dominant branches in the study of personalized recommendation. However, the presence of popular objects that meet the general interest of a broad spectrum of audience may introduce weak relationships between users and adversely influence the correct ranking of candidate objects. Besides, recent studies have also shown that gains of the accuracy in a recommendation may be frequently accompanied by losses of the diversity, making the selection of a reasonable tradeoff between the accuracy and the diversity not obvious. With these understandings, we propose in this paper a network-based collaborative filtering approach to overcome the adverse influence of popular objects while achieving a reasonable balance between the accuracy and the diversity. Our method starts with the construction of a user similarity network from historical data by using a nearest neighbor approach. Based on this network, we calculate discriminant scores for candidate objects and further sort the objects in non-ascending order to obtain the final ranking list. We validate the proposed approach by performing large-scale random sub-sampling experiments on two widely used data sets (MovieLens and Netflix), and we evaluate our method using two accuracy criteria and two diversity measures. Results show that our approach significantly outperforms the ordinary user-based collaborative filtering method by not only enhancing the recommendation accuracy but also improving the recommendation diversity.     

基于用户复杂网络特征分类的协同过滤模型

协同过滤算法被广泛应用的同时一直存在着伸缩性和可扩展性困难的问题。针对该问题,提出了一种基于用户复杂网络特征分类的推荐系统协同过滤模型。首先,在用户集中基于度值选择特征用户,建立相似性阈值实现非特征用户分组;然后,构建用户—用户相似性网络,通过K-core分解完成网络中的社区标记;最后,目标用户在组内选择邻居,实现电影评分预测。基于MovieLens和Netflix数据集的实验结果表明,该算法与经典协同过滤算法相比,提升了时间和空间的性能,展现了更为出色的伸缩性和可扩展性。