结合二部图投影与排序的协同过滤

协同过滤是推荐系统中应用最为广泛的方法.提出一类基于二部图一维投影与排序相结合的协同过滤算法,文中采用结构相似进行二部图投影并利用随机游走对节点排序.该方法不仅可以防止冷启动,具有较高准确度,且可扩展性良好.另外,该算法可以避免低覆盖率造成的推荐不准确.算法可以有两类不同的实现,分别是基于项协同过滤的项排序算法和基于用户协同过滤的用户排序算法,在标准数据集MovieLens上的测试表明了算法的有效性.     

基于时间加权的协同过滤算法研究

协同过滤算法是目前个性化推荐系统中应用最成功的推荐算法之一,但传统的算法没有考虑用户兴趣漂移的问题,导致推荐系统的推荐质量下降.针对这个问题,提出了基于时间加权的协同过滤算法.实验表明,改进的算法提高了推荐系统的推荐质量.     

基于加权三部图的协同过滤推荐算法

针对基于用户的协同过滤算法推荐结果过度集中在热门物品,导致多样性和新颖性较低、覆盖率较小的问题,文中提出基于加权三部图的协同过滤推荐算法.在分析数据稀疏和附加信息较少的基础上引入标签信息,可同时反映用户兴趣和物品属性,利用用户、物品和标签三元关系构建三部图.通过三部图网络映射到单模网络的方法获得用户偏好度,构建用户偏好...     

面向新闻推荐的用户兴趣模型构建与更新

针对新闻推荐系统中用户兴趣模型构建与用户兴趣漂移问题,提出了一种面向新闻推荐的用户兴趣模型构建与更新方法。首先采用向量空间模型与bisecting K-means聚类算法构建了原始用户兴趣模型;然后以艾宾浩斯遗忘曲线为基础构造了遗忘函数,并以此对用户兴趣模型进行时间加权,从而达到对用户兴趣模型更新的目的。实验以基于用户的协同过滤推荐、基于物品的协同过滤推荐为baseline,实验结果表明所构建的原始用户兴趣模型推荐性能更优,在◢F◣值上提升了4%,更新后的模型与原始模型相比◢F◣值提高了1.3%。     

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。     

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。     

一种属性和评分的协同过滤混合推荐算法

传统协同过滤推荐算法仅仅根据稀疏的评分矩阵向用户推荐,存在推荐质量不高的问题.提出了一种属性和评分的协同过滤混合推荐算法.该算法由项目的类别属性计算项目之间基于属性的相似性,考虑到用户兴趣随时间的变化,构建评分时间权重的指数函数,并应用到项目之间的Pearson相关相似性中.通过权重因子加权项目之间基于属性的相似性和项目之间的Pearson相关相似性,然后计算基于项目属性的评分预测.描绘职业分类树,构建职业相似性模型,并与性别加权结合产生用户综合属性的相似性,得到基于用户属性的评分预测.最后,综合两者计算混合评分预测.在Movielens实验数据集下,实验结果表明提出的算法具有较好的平均绝对误差.     

改进加权Slope one协同过滤推荐算法研究

协同过滤推荐是最成功的推荐技术之一,但数据稀疏性问题导致推荐准确度和推荐效率不高.针对这个问题,提出了一种改进的加权Slope one协同过滤推荐算法.计算用户之间的评分相似度,找出每个用户的最近邻;根据最近邻用户评分,使用基于用户的协同过滤和改进的加权Slope one算法的加权评分预测目标用户的未评分项目;给出推荐.实验过程中采用MovieLens数据集作为测试数据.实验结果表明:与原算法相比,算法提高了预测准确度,有效提高了推荐性能.     

结合项目类别和动态时间加权的协同过滤算法

基于项目的协同过滤算法仅通过计算项目相似性产生推荐结果,忽略了项目类别信息对项目相似性的影响,且未考虑时间因素对推荐结果产生的影响。针对上述问题,引入项目类别相似性、用户兴趣度时间加权函数和项目流行度时间加权函数,提出结合项目类别相似性和动态时间加权的协同过滤推荐算法,包括将项目类别相似性引入到传统项目相似性计算中。分析用户兴趣度和项目受欢迎程度随时间动态变化对推荐结果产生的影响,构造基于时间的用户兴趣度加权函数和基于时间的项目流行度加权函数。实验结果表明,该算法的项目类别特征能够进一步提高项目相似性的精度,动态时间加权函数能够及时反映用户兴趣度和项目受欢迎程度的变化,提高推荐的准确度。     

基于图嵌入模型的协同过滤推荐算法

传统协同过滤算法存在严重的数据稀疏和冷启动问题。利用社交网络中的丰富信息为解决传统协同过滤算法的数据稀疏和冷启动带来了契机。然而,传统基于社交网络的协同过滤算法仅利用粗粒度、稀疏的用户信任关系来改进传统协同过滤算法,即用0或1表示用户之间信任程度。另外,传统基于社交网络推荐算法仅仅集成用户之间显式信任关系,而忽略用户之间隐式的信任关系。本文提出一种基于图嵌入模型的协同过滤推荐算法,即利用图嵌入模型技术学习社交网络中用户的低维特征表示,并根据用户的低维特征表示推导用户之间细粒度的信任关系。最后,根据信任用户和相似用户对目标物品的评分权重预测用户对目标物品的评分。在真实数据集上的实验结果表明,基于图嵌入模型的协同过滤算法的性能优于传统的协同过滤算法。     

Enhancing collaborative filtering by user interest expansion via personalized ranking

Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored. To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative-filtering framework. The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem. Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin.     

Modeling and broadening temporal user interest in personalized news recommendation

User profiling is an important step for solving the problem of personalized news recommendation. Traditional user profiling techniques often construct profiles of users based on static historical data accessed by users. However, due to the frequent updating of news repository, it is possible that a user’s fine-grained reading preference would evolve over time while his/her long-term interest remains stable. Therefore, it is imperative to reason on such preference evaluation for user profiling in news recommenders. Besides, in content-based news recommenders, a user’s preference tends to be stable due to the mechanism of selecting similar content-wise news articles with respect to the user’s profile. To activate users’ reading motivations, a successful recommender needs to introduce “somewhat novel” articles to users.In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that a user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen as the recommended candidates based on the short-term user profile. We further propose to select news items from the user–item affinity graph using absorbing random walk model to increase the diversity of the recommended news list. Extensive empirical experiments on a collection of news data obtained from various popular news websites demonstrate the effectiveness of our method.     

基于知识图谱的用户偏好神经建模框架

针对目前基于特征和基于路径的知识图谱感知推荐方法的不足,文中提出端到端的将知识图谱引入推荐系统的用户偏好神经建模框架(NUPM).该框架以用户在知识图谱中的历史访问项目为偏好起点,通过知识图谱中实体间的关系链接传播用户偏好,学习用户的潜在偏好,同时使用注意力网络融合各传播阶段偏好特征以构建最终的用户偏好向量.在真实数据集上的对比实验表明文中框架在个性化推荐中对用户偏好刻画的有效性.     

Folkommender: a group recommender system based on a graph-based ranking algorithm

With the rapid popularity of smart devices, users are easily and conveniently accessing rich multimedia content. Consequentially, the increasing need for recommender services, from both individual users and groups of users, has arisen. In this paper, we present a new graph-based approach to a recommender system, called Folkommender, that can make recommendations most notably to groups of users. From rating information, we first model a signed graph that contains both positive and negative links between users and items. On this graph we examine two distinct random walks to separately quantify the degree to which a group of users would like or dislike items. We then employ a differential ranking approach for tailoring recommendations to the group. Our empirical evaluations on two real-world datasets demonstrate that the proposed group recommendation method performs better than existing alternatives. We also demonstrate the feasibility of Folkommender for smartphones.     

A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation

Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. Traditional collaborative approaches compute a similarity value between the current user and each other user by taking into account their rating style, that is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, collaborative algorithms compute recommendations for the current user. The problem with this approach is that the similarity value is only computable if users have common rated items. The main contribution of this work is a possible solution to overcome this limitation. We propose a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. In more detail, user profiles are clustered to discover current user neighbors. Content-based user profiles play a key role in the proposed hybrid recommender. Traditional keyword-based approaches to user profiling are unable to capture the semantics of user interests. A distinctive feature of our work is the integration of linguistic knowledge in the process of learning semantic user profiles representing user interests in a more effective way, compared to classical keyword-based profiles, due to a sense-based indexing. Semantic profiles are obtained by integrating machine learning algorithms for text categorization, namely a naïve Bayes approach and a relevance feedback method, with a word sense disambiguation strategy based exclusively on the lexical knowledge stored in the WordNet lexical database. Experiments carried out on a content-based extension of the EachMovie dataset show an improvement of the accuracy of sense-based profiles with respect to keyword-based ones, when coping with the task of classifying movies as interesting (or not) for the current user. An experimental session has been also performed in order to evaluate the proposed hybrid recommender system. The results highlight the improvement in the predictive accuracy of collaborative recommendations obtained by selecting like-minded users according to user profiles.     

基于项目属性的用户聚类协同过滤推荐算法

协同过滤推荐算法是个性化推荐服务系统的关键技术,由于项目空间上用户评分数据的极端稀疏性,传统推荐系统中的用户相似度量算法开销较大并且无法保证项目推荐精度.通过对共同感兴趣的项目属性的相似用户进行聚类,构建了不同项目评价的用户相似性,设计了一种优化的协同过滤推荐算法.实验结果表明,该算法能够有效避免由于数据稀疏性带来的弊端,提高了系统的推荐质量.     

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.     

Graph-based collaborative ranking

Data sparsity, that is a common problem in neighbor-based collaborative filtering domain, usually complicates the process of item recommendation. This problem is more serious in collaborative ranking domain, in which calculating the users' similarities and recommending items are based on ranking data. Some graph-based approaches have been proposed to address the data sparsity problem, but they suffer from two flaws. First, they fail to correctly model the users' priorities, and second, they can't be used when the only available data is a set of ranking instead of rating values.In this paper, we propose a novel graph-based approach, called GRank, that is designed for collaborative ranking domain. GRank can correctly model users’ priorities in a new tripartite graph structure, and analyze it to directly infer a recommendation list. The experimental results show a significant improvement in recommendation quality compared to the state of the art graph-based recommendation algorithms and other collaborative ranking techniques.     

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.     

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.