标签扩展的协同过滤推荐算法

大多数利用标签与用户和项目之间关系的推荐算法,都要面临用户个体不同所导致的标签稀疏问题,不同的用户为项目所标注的标签会有所不同.针对由于用户标注标签的随意性而导致的用户标签和项目标签矩阵稀疏问题,提出了一种标签扩展的协同过滤推荐算法.该算法根据用户标注标签的行为计算基于标签的标签相似度,根据用户标注的标签语义计算基于标签语义的标签相似度,从用户行为和标签语义2个方面评估标签的相似度,并利用标签相似度来扩展每个项目标签,降低由项目与标签的关联关系产生的矩阵稀疏度.在M ovieLens数据集上的实验结果表明,所提算法在精度上有所提高.     

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)....     

Content-based filtering for recommendation systems using multiattribute networks

Content-based filtering (CBF), one of the most successful recommendation techniques, is based on correlations between contents. CBF uses item information, represented as attributes, to calculate the similarities between items. In this study, we propose a novel CBF method that uses a multiattribute network to effectively reflect several attributes when calculating correlations to recommend items to users. In the network analysis, we measure the similarities between directly and indirectly linked items. Moreover, our proposed method employs centrality and clustering techniques to consider the mutual relationships among items, as well as determine the structural patterns of these interactions. This mechanism ensures that a variety of items are recommended to the user, which improves the performance. We compared the proposed approach with existing approaches using MovieLens data, and found that our approach outperformed existing methods in terms of accuracy and robustness. Our proposed method can address the sparsity problem and over-specialization problem that frequently affect recommender systems. Furthermore, the proposed method depends only on ratings data obtained from a user's own past information, and so it is not affected by the cold start problem.     

FastTagRec: fast tag recommendation for software information sites

Software information sites such as StackOverflow and Freeecode enable information sharing and communication for developers around the world. To facilitate correct classification and efficient search, developers need to provide tags for their postings. However, tagging is inherently an uncoordinated process that depends not only on developers’ understanding of their own postings but also on other factors, including developers’ English skills and knowledge about existing postings. As a result, developers keep creating new tags even though existing tags are sufficient. The net effect is an ever increasing number of tags with severe redundancy along with more postings over time. Any algorithms based on tags become less efficient and accurate. In this paper we propose FastTagRec, an automated scalable tag recommendation method using neural network-based classification. By learning existing postings and their tags from existing information, FastTagRec is able to very accurately infer tags for new postings. We have implemented a prototype tool and carried out experiments on ten software information sites. Our results show that FastTagRec is not only more accurate but also three orders of magnitude faster than the comparable state-of-the-art tool TagMulRec. In addition to empirical evaluation, we have also conducted an user study which successfully confirms the usefulness of of our approach.     

A language model approach for tag recommendation

Tags are user-generated keywords for entities. Recently tags have been used as a popular way to allow users to contribute metadata to large corpora on the web. However, tagging style websites lack the function of guaranteeing the quality of tags for other usages, like collaboration/community, clustering, and search, etc. Thus, as a remedy function, automatic tag recommendation which recommends a set of candidate tags for user to choice while tagging a certain document has recently drawn many attentions. In this paper, we introduce the statistical language model theory into tag recommendation problem named as language model for tag recommendation (LMTR), by converting the tag recommendation problem into a ranking problem and then modeling the correlation between tag and document with the language model framework. Furthermore, we leverage two different methods based on both keywords extraction and keywords expansion to collect candidate tag before ranking with LMTR to improve the performance of LMTR. Experiments on large-scale tagging datasets of both scientific and web documents indicate that our proposals are capable of making tag recommendation efficiently and effectively.     

Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.     

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.     

基于灰色关联度聚类与标签重叠因子结合的协同过滤推荐方法研究

协同过滤算法是目前被广泛运用在推荐系统领域的最成功技术之一,但是面对用户数量的快速增长及相应的评分数据的缺失,推荐系统中的数据稀疏性问题也越来越明显,严重地影响着推荐的质量和效率。针对传统协同过滤算法中的稀疏性问题,采用了基于灰色关联度的方法对用户评分矩阵进行数据标准化处理,得到用户关联度并形成关联度矩阵;然后对关联矩阵中的用户进行关联度聚类,以减少相似性算法的复杂度;之后利用标签重叠因子对传统计算用户相似性的协同过滤算法进行改进,将重叠因子与用户评分以非线性形式进行组合;最后通过实例改进后的算法在推荐精确度上有着较大的提高。     

基于潜在标签挖掘和细粒度偏好的个性化标签推荐

为进一步提高个性化标签推荐性能,针对标签数据的稀疏性以及传统方法忽略隐藏在用户和项目上下文中潜在标签的缺陷,提出一种基于潜在标签挖掘和细粒度偏好的个性化标签推荐方法。首先,提出利用用户和项目的上下文信息从大量未观测标签中挖掘用户可能感兴趣的少量潜在标签,将标签重新划分为正类标签、潜在标签和负类标签三类,进而构建〈用户,项目〉对标签的细粒度偏好关系,在缓解标签稀疏性的同时,提高对标签偏好关系的表达能力;然后,基于贝叶斯个性化排序优化框架对细粒度偏好关系进行建模,并结合成对交互张量分解对偏好值进行预测,构建细粒度的个性化标签推荐模型并提出优化算法。对比实验表明,提出的方法在保证较快收敛速度的前提下,有效地提高了个性化标签的推荐准确性。     

标签模糊和冗余在标签推荐中的研究及应用

现有的Folksonomy标签推荐系统使用的推荐算法没有考虑标签模糊和冗余问题,影响了用户建模和对推荐系统评估的准确性,并且降低了系统的推荐质量,增加了用户选择喜好项目时的负担。通过对标签推荐系统的研究,将标签模糊和冗余应用到标签推荐算法当中,有助于提高系统的推荐质量,并且能提供更合理的评价方法。实验结果表明：经过标签模糊和冗余处理的标签推荐算法显著地提高了推荐系统的推荐质量。     

Div-clustering: Exploring active users for social collaborative recommendation

Collaborative recommendation (CR) is a popular method of filtering items that may interest social users by referring to the opinions of friends and acquaintances in the network and computer applications. However, CR involves a cold-start problem, in which a newly established recommender system usually exhibits low recommending accuracy because of insufficient data, such as lack of ratings from users. In this study, we rigorously identify active users in social networks, who are likely to share and accept a recommendation in each data cluster to enhance the performance of the recommendation system and solve the cold-start problem. This novel modified CR method called div-clustering is presented to cluster Web entities in which the properties are specified formally in a recommendation framework, with the reusability of the user modeling component considered. We improve the traditional k-means clustering algorithm by applying supplementary works such as compensating for nominal values supported by the knowledge base, as well as computing and updating the k value. We use the data from two different cases to test for accuracy and demonstrate high quality in div-clustering against a baseline CR algorithm. The experimental results of both offline and online evaluations, which also consider in detail the volunteer profiles, indicate that the CR system with div-clustering obtains more accurate results than does the baseline system.     

Extended latent class models for collaborative recommendation

With the advent of the World Wide Web, providing just-in-time personalized product recommendations to customers now becomes possible. Collaborative recommender systems utilize correlation between customer preference ratings to identify "like-minded" customers and predict their product preference. One factor determining the success of the recommender systems is the prediction accuracy, which in many cases is limited by lacking adequate ratings (the sparsity problem). Recently, the use of latent class model (LCM) has been proposed to alleviate this problem. In this paper, we first study how the LCM can be extended to handle customers and products outside the training set. In addition, we propose the use of a pair of LCMs (called dual latent class model-DLCM), instead of a single LCM, to model customers' likes and dislikes separately for enhancing the prediction accuracy. Experimental results based on the EachMovie dataset show that DLCM outperforms both LCM and the conventional correlation-based method when the available ratings are sparse.     

一种基于动机倾向的标签推荐方法

为了能够推荐符合用户信息需求的标签,在深入分析社会标签空间和传统标签推荐方法的基础上,提出了度量用户和资源的动机倾向性的五种指标,并对其测度有效性进行了验证。基于此指标体系,建立了动机倾向性判别模型,并设计了推荐算法。实验结果表明,基于动机倾向的推荐算法比当前主流推荐算法具有更加准确的推荐结果。     

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.     

A framework for empirical evaluation of conceptual modeling techniques

The paper presents a framework for the empirical evaluation of conceptual modeling techniques used in requirements engineering. The framework is based on the notion that modeling techniques should be compared via their underlying grammars. The framework identifies two types of dimensions in empirical comparisons—affecting and affected dimensions. The affecting dimensions provide guidance for task definition, independent variables and controls, while the affected dimensions define the possible mediating variables and dependent variables. In particular, the framework addresses the dependence between the modeling task—model creation and model interpretation—and the performance measures of the modeling grammar. The utility of the framework is demonstrated by using it to categorize existing work on evaluating modeling techniques. The paper also discusses theoretical foundations that can guide hypothesis generation and measurement of variables. Finally, the paper addresses possible levels for categorical variables and ways to measure interval variables, especially the grammar performance measures.     

基于网络和标签的混合推荐算法

基于网络结构的推荐算法利用用户与项目间的结构关系进行推荐,忽略了用户偏好,而项目的标签隐含了项目的内容及用户的偏好,提出一种基于网络结构和标签的混合推荐方法。算法根据用户选择项目的标签统计信息,分别采用TF-IDF和用户对标签的支持度两种方法构建用户偏好模型,与基于网络的推荐模型进行线性组合推荐。通过在基准数据集MovieLens上测试证明,该算法在推荐结果命中率、个性化程度、多样性等方面均优于基于网络的推荐算法。     

基于经验分布和KL散度的协同过滤推荐质量评价研究

提出了一种基于经验分布和KL散度的协同过滤推荐质量评价方法RQE-EDKL（recommendation quality evaluation based on empirical distribution and KL divergence）。RQE-EDKL首先利用历史用户—商品数据生成不同商品数量下的商品历史使用概率分布；然后，利用该分布与各个协同过滤推荐方法得到的用户商品使用概率进行比较，计算其KL散度；最后，将KL散度最小的推荐结果视为最佳推荐结果并推送给用户。在TalkingData数据集上的实验结果表明，RQE-EDKL评价方法能够有效地在不同的推荐结果中选择更为切合用户真实需求的推荐结果，从而提高了协同过滤推荐的质量。     

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.