基于标准化高斯pLSA协同过滤的用电量预测模型

现有的电力负荷预测算法在中长期预测时存在不同程度的局限性.究其原因,是因为影响复杂非线性系统输出的变元过多,难以用解析的方法对其进行描述.本文提出利用概率潜在语义分析使历史随机数据呈现出各种有规律的示象(aspect),结合对内容的协同过滤技术去建立用电量预测模型,从而利用统计学习的方法避开了对影响系统输出的隐含变元的寻找与刻画.采用MATLAB进行数值仿真实验的结果表明该算法相比于神经网络和灰色预测在准确度方面具有优势.     

Fuzzy-genetic approach to recommender systems based on a novel hybrid user model

The main strengths of collaborative filtering (CF), the most successful and widely used filtering technique for recommender systems, are its cross-genre or ‘outside the box’ recommendation ability and that it is completely independent of any machine-readable representation of the items being recommended. However, CF suffers from sparsity, scalability, and loss of neighbor transitivity. CF techniques are either memory-based or model-based. While the former is more accurate, its scalability compared to model-based is poor. An important contribution of this paper is a hybrid fuzzy-genetic approach to recommender systems that retains the accuracy of memory-based CF and the scalability of model-based CF. Using hybrid features, a novel user model is built that helped in achieving significant reduction in system complexity, sparsity, and made the neighbor transitivity relationship hold. The user model is employed to find a set of like-minded users within which a memory-based search is carried out. This set is much smaller than the entire set, thus improving system’s scalability. Besides our proposed approaches are scalable and compact in size, computational results reveal that they outperform the classical approach.     

A Maximum Margin Matrix Factorization based Transfer Learning Approach for Cross-Domain Recommendation

Recommender systems usually employ techniques like collaborative filtering for providing recommendations on items/services. Maximum Margin Matrix Factorization (MMMF) is an effective collaborative filtering approach. MMMF suffers from the data sparsity problem, i.e., the number of items rated by the users are very small as compared to the very large item space. Recently, techniques like cross-domain collaborative filtering (transfer learning) is suggested for addressing the data sparsity problem. In this paper, we propose a model for transfer learning in collaborative filtering through MMMF to address the data sparsity issue. The latent feature matrices involved in MMMF are clustered and combined to generate a cluster-level rating pattern called codebook and a codebook transfer is used for transfer of information. Transferring of codebook and finding the predicted rating matrix is done in a novel way by introducing a softness constraint into the optimization function. We have experimented our methods with different levels of sparsity using benchmark datasets. Results from experiments show that our model approximates the target matrix well.     

A hybrid approach for movie recommendation

Collaborative and content-based filtering are the major methods in recommender systems that predict new items that users would find interesting. Each method has advantages and shortcomings of its own and is best applied in specific situations. Hybrid approaches use elements of both methods to improve performance and overcome shortcomings. In this paper, we propose a hybrid approach based on content-based and collaborative filtering, implemented in MoRe, a movie recommendation system. We also provide empirical comparison of the hybrid approach to the base methods of collaborative and content-based filtering and draw useful conclusions upon their performance.     

Informed Recommender: Basing Recommendations on Consumer Product Reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology.     

基于认知度的协同过滤推荐算法

协同过滤技术被广泛应用于各种推荐系统当中.基于内存的协同过滤算法通过比较目标用户与其他用户的已有评分,为目标用户的未评分项目作出相应的预测.提出了一种新的基于内存的算法.根据项目的关键属性对它们进行分类,通过计算用户对各类项目的认知度,为目标用户选择相似用户并预测评分.通过MovieLens数据集的实验结果表明,该算法可以有效地解决包括数据稀疏性和新用户在内的一些协同过滤的基本问题,提供更高质量的推荐.     

基于图卷积的双通道协同过滤推荐算法

在推荐系统中,利用图卷积网络等方法提取图的高阶信息缓解了冷启动问题。为了在此基础上融合神经网络协同过滤的深层特征提取能力,提出一种基于图卷积的双通道协同过滤推荐算法（GCNCF-2C）。首先,将推荐问题分为上游任务和下游任务;其次,在上游任务中,预训练编码器利用包含残差的一维卷积层和多个图卷积层在两个独立通道中对节点特征和图高阶特征进行分离提取,形成节点的特征表示;最后,解码器通过节点特征进行评级预测,进行端到端的训练。在数据集MovieLens-100K和MovieLens-1M上的实验表明,该算法相比于基线模型在两个数据集上的RMSE指标平均提高1.72%和1.76%,MAE指标平均提高2.7%和1.98%,同时在基于用户和项目的冷启动实验中RMSE指标平均提高5.9%,具有更好的综合性能。     

Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation

We propose a collaborative filtering method to provide an enhanced recommendation quality derived from user-created tags. Collaborative tagging is employed as an approach in order to grasp and filter users’ preferences for items. In addition, we explore several advantages of collaborative tagging for data sparseness and a cold-start user. These applications are notable challenges in collaborative filtering. We present empirical experiments using a real dataset from del.icio.us. Experimental results show that the proposed algorithm offers significant advantages both in terms of improving the recommendation quality for sparse data and in dealing with cold-start users as compared to existing work.     

Detection of the customer time-variant pattern for improving recommender systems

Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems—content-based recommending and collaborative filtering. So far, collaborative filtering recommender systems have been very successful in both information filtering and e-commerce domains. However, the current research on recommendation has paid little attention to the use of time-related data in the recommendation process. Up to now there has not been any study on collaborative filtering to reflect changes in user interest.This paper suggests a methodology for detecting a user's time-variant pattern in order to improve the performance of collaborative filtering recommendations. The methodology consists of three phases of profiling, detecting changes, and recommendations. The proposed methodology detects changes in customer behavior using the customer data at different periods of time and improves the performance of recommendations using information on changes.     

个性化信息推荐方法研究

随着信息技术和互联网的发展,人们进入了信息过量且愈发碎片化的时代。当前,个性化信息推送是用户获取网络信息的有效渠道。由于信息的更新速度快和用户兴趣更新等问题,传统的推荐算法很少关注甚至忽略上述因素,造成最终的推荐结果欠佳。为了给用户更好的个性化推荐服务,论文首次引入截取因子,提出了组合推荐算法（CR算法）。该算法的实质是将截取因子引入到基于内容的推荐算法与基于用户的协同过滤算法中,进而生成混合推荐算法。在推荐列表中,CR算法产生的推荐结果由两部分组成:一部分由混合推荐算法生成,另一部分由基于用户的协同过滤算法生成。根据信息的发布时间,决定该信息由哪类算法产生推荐:当浏览时间与当前时间的间隔不大于某个值时,采用混合推荐算法;否则,直接采用基于用户的协同过滤算法。基于真实数据的实验结果表明,CR算法优于同类算法。     

Probabilistic memory-based collaborative filtering

Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper, we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem." Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real-world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences.     

融合用户偏好和多交互网络的协同过滤算法

推荐系统中的辅助信息可以为推荐提供有用的帮助,而传统的协同过滤算法在计算用户相似度时对辅助信息的利用率低,数据稀疏性大,导致推荐的精度偏低.针对这一问题,本文提出了一种融合用户偏好和多交互网络的协同过滤算法(NIAP-CF).该算法首先根据评分矩阵和项目属性特征矩阵挖掘出用户的项目属性偏好信息,然后使用SBM方法计算用户间的项目属性偏好相似度,并用其改进用户相似度计算公式.在进行评分预测时,构建融合用户-项目属性偏好信息的多交互神经网络预测模型,使用动态权衡参数综合由用户相似度计算出的预测评分和模型的预测评分来进行项目推荐.本文使用MovieLens数据集进行实验验证,实验结果表明改进算法能够提高推荐的精度,降低评分预测的MAE和RMSE值.     

Personalisation in web computing and informatics: Theories, techniques, applications, and future research

Recently, personalised search engines and recommendation systems have been widely adopted by users who require assistance in searching, classifying, and filtering information. This paper presents an overview of the field of personalisation systems and describes current state-of-the-art methods and techniques. It reviews approaches for (1) user profiling, including behaviour, preference, and intention modelling; (2) content modelling, comprising content representation, analysis, and classification; and (3) filtering methods for recommendation, classified into four main categories: rule-based, content-based, collaborative, and hybrid filtering. The paper also discusses personalisation systems in different domains, and various techniques and their limitations. Finally, it identifies several issues and possible directions for further research that can improve recommendation capabilities and enhance personalised systems.     

基于自适应图卷积注意力神经协同推荐算法

随着互联网的快速发展,推荐系统可以用来处理信息过载的问题。由于传统推荐系统的诸多问题导致其无法处理发掘隐藏信息,提出一种自适应图卷积注意力神经协同推荐算法（ANGCACF）。首先获取用户和项目交互图,通过图卷积神经网络自适应的聚合用户和项目特征信息;其次对用户和项目特征信息添加自适应扩充数据,以解决数据稀疏性,利用注意力机制对用户和项目特征信息及添加的自适应扩充数据重新分配权重;最后将得到的用户和项目特征表示使用基于矩阵分解的协同过滤的算法框架得出最终推荐结果。在MovieLens-1M、MovieLens-100K和 Amazon-baby三个公开数据集上的实验表明,该算法在推荐准确率、召回率、MRR、命中率和 NDCG 五个指标上均优于基线方法。     

基于深度学习的论文个性化推荐算法

该文基于学术搜索和数据挖掘平台Aminer向用户进行个性化推荐,提出了结合协同过滤推荐和基于内容推荐的混合模型,实验表明该算法可以有效解决新物品的推荐问题,即冷启动问题。其中在基于内容推荐的模型中,融合深度学习的方法,引进了词向量模型,将用户和论文映射到用词向量空间, 并使用WMD(Word Mover Distance)计算相似度。实验表明,与其他基线模型相比该文提出的推荐模型在准确率上显著提高了4%。     

基于融合元路径的图神经网络协同过滤算法

传统的协同过滤算法没有充分考虑用户和商品的交互信息,且面临数据稀疏、冷启动等问题,造成了推荐系统的结果不准确.在本文中提出了一种新的推荐算法,即基于融合元路径的图神经网络协同过滤算法.该算法首先由二部图嵌入用户和商品的历史互动,并通过多层神经网络传播获取用户和商品的高阶特征;然后基于元路径的随机游走来获取异质信息网络中...     

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。     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.