关联集合在协同过滤推荐算法中的应用研究

为了提高电子商务推荐系统的精度,提出了基于关联集合的协同过滤推荐算法,该算法通过频繁项集生成算法生成一系列频繁项集,然后通过合并处理过滤掉与用户关联很小的一些噪音项目,从而使协同过滤算法更加有效。该算法在推荐精度上比传统的方法优越。     

一种引入内容特征的混合推荐方法

本文使用聚类算法将项目和用户进行分组,从而引入内容特征,再结合协同过滤方法,构造一种混合的推荐方法。实验结果表明,本文的推荐方法在较高稀疏度下优于一般的协同过滤算法。     

基于协同过滤的电子商务个性化推荐算法分析

个性化推荐系统是电子商务运作过程中的重要组成部分,计算机网络信息化的不断发展和当今数据的爆炸式增长,使得人们很难在海量信息中找寻出适合自身需求的数据和信息,信息过载状况时有发生。阐述了我国电子商务个性化推荐系统的作用,分析了当前主流电子商务推荐技术,对基于协同过滤的电子商务个性推荐算法进行了研究。     

融合相似性评价、信任度与社会网络的学术资源推荐方法研究

针对数字图书馆学术资源信息过载问题,提出了一种融合相似性评价、信任度与社会网络的学术资源推荐方法.该方法利用信任度分析与社会网络关系挖掘技术对协同过滤推荐方法进行了改进,并综合考虑了用户特征因素对推荐结果的影响.实验结果表明,本方法在预测准确度和覆盖率指标上均优于其他推荐方法,显著提高了学术资源推荐系统的推荐质量.     

基于协同过滤的高校图书馆纸本资源智能推荐方法实证研究

[目的/意义]旨在为高校图书馆提高纸质资源采购质量与利用率提供参考。[方法/过程]基于用户画像的理论对不同用户进行多维度的刻画,利用机器学习中监督学习的方法,通过采用协同过滤的推荐算法对用户偏好特征做精细统计分析的定量化计算,并从用户需求的角度建立用户偏好同步变化的自适应优化在线学习的纸本资源推荐系统。[结果/结论]该研究从实证分析角度为用户实现精准的个性化纸本资源推荐服务,为高校图书馆纸质文献检索库实现智能偏好的检索功能,建立纸质文献检索库合理有效的动态更新机制,提升用户体验。     

一种基于用户聚类的协同过滤个性化图书推荐系统

综合协同过滤技术和聚类技术，提出了一种个性化图书推荐系统的具体实现方案。系统对图书馆数据库保留的大量用户图书借阅记录进行挖掘，向用户提供个性化图书推荐，为图书馆个性化服务的研究和实践提供了一种新的思路。     

基于模糊推理的web客户需求协同过滤推荐算法

文章提出了基于模糊推理的协同过滤推荐算法,在该算法中,用模糊集合之间特征系数来代替传统算法的相似系数,用模糊推理来代替加权平均预测.最后文章通过实验证明该算法具有较好的精确度,为以后研究推荐算法提供了一种新的途径.     

基于模糊聚类和资源平滑的协同过滤推荐

在分析目前电子商务推荐系统及传统的协同过滤推荐存在问题的基础上,提出了一种新的电子商务推荐算法。该算法利用客户对商品的历史评分记录中所隐含的客户相关信息和商品相关信息来为客户推荐商品,并且将模糊聚类技术运用于商品最近邻居和客户最近邻居的查找。实验结果表明该算法能够提供更好的推荐,聚类数对推荐质量有较大的影响。     

一种基于订阅记录的图书协同过滤推荐方法研究

【目的/意义】通过订阅记录获取用户兴趣爱好,并将协同过滤推荐方法应用于图书个性化推荐,为读者提供优质服务。【方法/过程】以协同过滤算法为基础,根据用户订阅记录,分别计算用户相似性和订阅图书相似性。针对传统协同过滤方法在计算热门订阅相似度时存在的缺陷,引入对订阅权重的惩罚机制,减轻了热门订阅会和很多订阅相似的可能性,并根据协同过滤方法,产生相应推荐结果。【结果/结论】运用公开可获取的数据集进行的算法验证表明,基于订阅记录的协同过滤算法推荐准确度较高,对提升用户图书借阅体验相关研究与实践有一定的参考价值。     

一种基于支持向量机回归的推荐算法

随着电子商务的迅速发展，推荐系统与算法已经成为理论研究的热点。支持向量机是一种强大的分类工具，由其衍生出的支持向量机回归方法能很好地解决非线性回归问题。文中以电影推荐为例，引入支持向量机回归方法来分析项目的内容，构建用户模型，进而给出推荐。实验结果和理论分析表明这种推荐算法与传统协同过滤算法相比，能够明显提高推荐精度，并显著缩短了推荐所需时间；在大样本量情况下也能同样高效。     

Hybrid collaborative filtering model for consumer dynamic service recommendation based on mobile cloud information system

The rapid development of the web has led to a considerable increase in information dissemination. Recently, personalized web service recommendation has become a popular research area in service computing. Research on web service recommendation systems mainly addresses two problems: prediction and completion of sparse QoS data, and the user's personalized recommendation. To address the issue of high data sparsity and low recommendation accuracy in the traditional service recommendation models under mobile cloud, this study presents a hybrid collaborative filtering model for consumer service recommendation based on mobile cloud by introducing user preferences. The example verified that the service recommendation based on the model can effectively reduce the data sparsity and increase the accuracy of the prediction.     

Cluster searching strategies for collaborative recommendation systems

In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster-skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures.     

Building user profiles based on sequences for content and collaborative filtering

Modeling user profiles is a necessary step for most information filtering systems – such as recommender systems – to provide personalized recommendations. However, most of them work with users or items as vectors, by applying different types of mathematical operations between them and neglecting sequential or content-based information. Hence, in this paper we study how to propose an adaptive mechanism to obtain user sequences using different sources of information, allowing the generation of hybrid recommendations as a seamless, transparent technique from the system viewpoint. As a proof of concept, we develop the Longest Common Subsequence (LCS) algorithm as a similarity metric to compare the user sequences, where, in the process of adapting this algorithm to recommendation, we include different parameters to control the efficiency by reducing the information used in the algorithm (preference filter), to decide when a neighbor is considered useful enough to be included in the process (confidence filter), to identify whether two interactions are equivalent (δ-matching threshold), and to normalize the length of the LCS in a bounded interval (normalization functions). These parameters can be extended to work with any type of sequential algorithm.We evaluate our approach with several state-of-the-art recommendation algorithms using different evaluation metrics measuring the accuracy, diversity, and novelty of the recommendations, and analyze the impact of the proposed parameters. We have found that our approach offers a competitive performance, outperforming content, collaborative, and hybrid baselines, and producing positive results when either content- or rating-based information is exploited.     

基于数字相关法的多通道DAC同步算法

针对多通道MUXDAC系统的同步问题,采用数字相关法进行反馈时钟鉴相,提出一种适于FPGA的低采样率反馈算法.对算法原理、实现方法及影响算法精度的各参数进行了分析.该算法在雷达双通道信号发生器系统中得以应用,证明了其正确性.算法简单易行,提高了系统可靠性.     

Connecting user and item perspectives in popularity debiasing for collaborative recommendation

Recommender systems learn from historical users’ feedback that is often non-uniformly distributed across items. As a consequence, these systems may end up suggesting popular items more than niche items progressively, even when the latter would be of interest for users. This can hamper several core qualities of the recommended lists (e.g., novelty, coverage, diversity), impacting on the future success of the underlying platform itself. In this paper, we formalize two novel metrics that quantify how much a recommender system equally treats items along the popularity tail. The first one encourages equal probability of being recommended across items, while the second one encourages true positive rates for items to be equal. We characterize the recommendations of representative algorithms by means of the proposed metrics, and we show that the item probability of being recommended and the item true positive rate are biased against the item popularity. To promote a more equal treatment of items along the popularity tail, we propose an in-processing approach aimed at minimizing the biased correlation between user-item relevance and item popularity. Extensive experiments show that, with small losses in accuracy, our popularity-mitigation approach leads to important gains in beyond-accuracy recommendation quality.     

Item diversified recommendation based on influence diffusion

Recently, the high popularity of social networks accelerates the development of item recommendation. Integrating the influence diffusion of social networks in recommendation systems is a challenging task since topic distribution over users and items is latent and user topic interest may change over time. In this paper, we propose a dynamic generative model for item recommendation which captures the potential influence logs based on the community-level topic influence diffusion to infer the latent topic distribution over users and items. Our model enables tracking the time-varying distributions of topic interest and topic popularity over communities in social networks. A collapsed Gibbs sampling algorithm is proposed to train the model, and an improved diversification algorithm is proposed to obtain item diversified recommendation list. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method. The results validate our approach and show the superiority of our method compared with state-of-the-art diversified recommendation methods.     

Multi-feature fused collaborative attention network for sequential recommendation with semantic-enriched contrastive learning

Recently, graph neural network (GNN) has been widely used in sequential recommendation because of its powerful ability to capture high-order collaborative relations, greatly promoting recommendation performance. However, some existing GNN-based methods fail to make full use of multiple relevant features of nodes and ignore the impact of semantic association between nodes on extracting user preferences. To this end, we propose a multi-feature fused collaborative attention network MASR, which sufficiently learns the temporal and positional features of nodes, and innovatively measures the importance of these two features for analyzing the nodes’ dynamic patterns. In addition, we incorporate semantic-enriched contrastive learning into collaborative filtering to enhance the semantic association between nodes and reduce the noise from the structural neighborhood, which has a positive effect on the sequential recommendation. Compared with the baseline models, the performance of MASR on MovieLens, CDs and Beauty datasets is improved by 2.0%, 2.1% and 1.7% respectively, proving its effectiveness in the sequential recommendation.     

基于共同评分的协同过滤算法

协同过滤是目前电子商务推荐系统中使用最广泛最成功的一种个性化推荐算法.受数据稀疏性影响,传统协同过滤算法在较小共同评分项集上计算出的相似度不能准确反映用户间的相似关系,严重影响了推荐系统的精度.针对该问题,在分析共同评分分布及其与相似度关系的基础上,提出了基于共同评分的协同过滤算法,无须计算相似度,直接将共同评分作为最近邻选择标准.MovieLens实验表明该算法能明显提高预测结果的准确性和覆盖率.     

基于用户偏好的文献推荐系统

本系统基于模糊联想记忆神经网络,建立偏好评价模型,根据用户偏好对搜索引擎搜索到的候选文献进行评级,为用户推荐偏好值高的文献。本系统的学习模块采用PCA-CG算法和误差反向传播算法,以用户阅读过的基准文献和其对应评级作为训练样本,对用户偏好进行学习;推理模块根据学习到的模糊规则和隶属函数来计算候选文献的偏好值,并以偏好值排序,把偏好值高的文献推荐给用户。把该模型应用于信息技术类文献的检索,实验表明系统提供的推荐文献具有较高可信度。