面向图书馆大数据知识服务的多情境兴趣推荐方法     

刘海鸥  孙晶晶  苏妍嫄  张亚明 《现代情报》2018,38(6):62

大数据环境下,推荐系统项目评分的稀疏性问题愈加突出,单兴趣表示方法也难以对用户多种情境兴趣进行准确描述,导致推荐结果精度大大降低。鉴于此,提出一种多情境兴趣表示方法,在此基础上构建面向图书馆大数据知识服务的多情境兴趣推荐模型,通过对用户多情境兴趣的层次划分,利用蚁群层次挖掘的优势来发现目标用户的若干最近邻类簇,然后根据类簇内相似用户对目标项目的评分对未评分项目进行预测,最后结合MapReduce化的大数据并行处理方法来进行协同过滤推荐。实验结果表明,多情境兴趣的建模方法改善了单兴趣建模存在的歧义推荐问题,有效缓解了大数据环境下项目评分的数据稀疏问题,MapReduce化的蚁群层次聚类方法也大大改善了推荐系统的运行效率。  相似文献   

Enhancing sequential recommendation with contrastive Generative Adversarial Network     

《Information processing & management》2023,60(3):103331

Sequential recommendation models a user’s historical sequence to predict future items. Existing studies utilize deep learning methods and contrastive learning for data augmentation to alleviate data sparsity. However, these existing methods cannot learn accurate high-quality item representations while augmenting data. In addition, they usually ignore data noise and user cold-start issues. To solve the above issues, we investigate the possibility of Generative Adversarial Network (GAN) with contrastive learning for sequential recommendation to balance data sparsity and noise. Specifically, we propose a new framework, Enhanced Contrastive Learning with Generative Adversarial Network for Sequential Recommendation (ECGAN-Rec), which models the training process as a GAN and recommendation task as the main task of the discriminator. We design a sequence augmentation module and a contrastive GAN module to implement both data-level and model-level augmentations. In addition, the contrastive GAN learns more accurate high-quality item representations to alleviate data noise after data augmentation. Furthermore, we propose an enhanced Transformer recommender based on GAN to optimize the performance of the model. Experimental results on three open datasets validate the efficiency and effectiveness of the proposed model and the ability of the model to balance data noise and data sparsity. Specifically, the improvement of ECGAN-Rec in two evaluation metrics (HR@N and NDCG@N) compared to the state-of-the-art model performance on the Beauty, Sports and Yelp datasets are 34.95%, 36.68%, and 13.66%, respectively. Our implemented model is available via https://github.com/nishawn/ECGANRec-master.  相似文献   

Personalized fashion outfit generation with user coordination preference learning     

《Information processing & management》2023,60(5):103434

This paper focuses on personalized outfit generation, aiming to generate compatible fashion outfits catering to given users. Personalized recommendation by generating outfits of compatible items is an emerging task in the recommendation community with great commercial value but less explored. The task requires to explore both user-outfit personalization and outfit compatibility, any of which is challenging due to the huge learning space resulted from large number of items, users, and possible outfit options. To specify the user preference on outfits and regulate the outfit compatibility modeling, we propose to incorporate coordination knowledge in fashion. Inspired by the fact that users might have coordination preference in terms of category combination, we first define category combinations as templates and propose to model user-template relationship to capture users’ coordination preferences. Moreover, since a small number of templates can cover the majority of fashion outfits, leveraging templates is also promising to guide the outfit generation process. In this paper, we propose Template-guided Outfit Generation (TOG) framework, which unifies the learning of user-template interaction, user–item interaction and outfit compatibility modeling. The personal preference modeling and outfit generation are organically blended together in our problem formulation, and therefore can be achieved simultaneously. Furthermore, we propose new evaluation protocols to evaluate different models from both the personalization and compatibility perspectives. Extensive experiments on two public datasets have demonstrated that the proposed TOG can achieve preferable performance in both evaluation perspectives, namely outperforming the most competitive baseline BGN by 7.8% and 10.3% in terms of personalization precision on iFashion and Polyvore datasets, respectively, and improving the compatibility of the generated outfits by over 2%.  相似文献   

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

《Information processing & management》2023,60(5):103416

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.  相似文献   

基于数据挖掘的图书智能推荐系统研究   总被引：2，自引：0，他引：2  

丁雪 《情报理论与实践》2010,33(5)

针对目前传统数字图书馆无法为用户提供准确个性的图书推荐服务的问题,提出构建基于数据挖掘技术的图书智能推荐系统,简单分析数据挖掘技术中关联规则技术适用图书推荐的原因和相关概念,并且对该系统的框架进行研究,最后通过实验,运用数据挖掘软件对真实的借阅记录进行关联规则挖掘,得出关联规则作为图书智能推荐系统的关键技术是行之有效的结论。  相似文献   

面向移动健康医疗系统的多层二分网络推荐算法     

周岩  雷世尧  张灿 《中国科学院大学学报》2017,34(1):112-118

移动健康医疗系统是信息搜索、精准服务和信息过滤的重要手段,有效提升现有医疗资源的使用效率。为提高健康资讯推荐效率和准确性,提出一种多层二分网络推荐算法,将用户评价标准扩展为“感兴趣”、“不感兴趣”和“未知”3种级别;同时,根据用户感兴趣的信息类别,将原有的“用户-信息”层改进为“用户-信息-类别”层,使置信度在移动医疗多层网络中迭代传播,优化分级医疗资源的使用效率。实验结果表明,多层二分网络推荐算法提高了移动健康医疗系统的服务效率。  相似文献   

新闻推荐系统研究综述     

孟开元  岳宇航  曹庆年 《教育技术导刊》2021,20(1):249-252

通过网络阅读新闻已经成为一种广泛流行的阅读方式,随着网络新闻资源的日益普及,在海量的新闻中,用户很容易被一些偏离自己兴趣爱好的信息所淹没,这种现状促进了新闻推荐系统的发展,其旨在帮助用户在巨大的动态新闻空间发现心仪的新闻,提高用户满意度.分析新闻推荐系统研究过程和现状,对新闻推荐系统的4个关键技术进行重点分析,并比较其...  相似文献   

Active tag recommendation for interactive entity search: Interaction effectiveness and retrieval performance     

《Information processing & management》2022,59(2):102856

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图书馆荐购系统研究现状、趋势与启示   总被引：1，自引：0，他引：1  

赵英 《大学图书情报学刊》2021,39(2):82-87

采用文献调查法和文献计量法,梳理图书馆荐购系统研究现状,分析荐购系统产生与发展的驱动因素、荐购系统类型及其实现关键,并在此基础上探索荐购系统未来的发展趋势,为后续荐购系统的实践和研究提供参考。  相似文献