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1.
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
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
2.
近年来,互联网和web技术的不断进步促进了电子商务推荐系统的快速发展,各种推荐系统的日益繁荣改变了传统的贸易行为,它的逐步建立和完善使传统的商务运作摆脱了已有规则的束缚,对相关的商业形态、交易形式、流通方式以及营销方式等都产生的巨大的影响。针对电子商务推荐系统面临的主要挑战,对电子商务推荐系统推荐算法设计以及推荐系统体系结构等关键技术进行了有益的探索和研究,对算法中影响推荐质量的稀疏性问题和影响用户满意度的推荐完整性问题进行深入分析,引入了基于聚类的最近邻查询技术对协同过滤算法进行改进,经分析新算法缓解了对特殊用户无法产生准确推荐的问题,能够带给用户各更准确的推荐。 相似文献
3.
个性化信息服务中用户偏好的动态挖掘 总被引：2，自引：0，他引：2
基于个性化信息服务中用户偏好随时间变化的特性,采用聚类、关联规则等技术,对用户偏好进行动态挖掘.通过追踪用户需求序列,最终产生Top-N产品推荐,旨在提高推荐系统的推荐质量.然后选取协同过滤算法作对照,并采用MovieLens站点提供的测试数据集.通过对召回率和精度两项指标的分析,表明该动态挖掘算法具有较高的推荐准确度和全面性. 相似文献
4.
一种基于用户聚类的协同过滤个性化图书推荐系统 总被引：1，自引：1，他引：1
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。 相似文献
5.
《科技广场》2016,(6)
以阿里为代表的巨头电商平台创造了一个又一个"双十一"促销奇迹,这得益于个性化推荐系统,协同过滤是其中应用广泛、效果较准确的一种技术,也是目前主流的推荐算法。本文采用基于用户协同过滤的个性化推荐技术对2014年"天池"大数据平台提供的四个月内884个真实用户对9531个商品的真实点击浏览、加入购物车、收藏和购买等四种行为的访问记录进行分析,以便对用户进行商品推荐。为真实反映用户对物品的偏好程度,对点击浏览、收藏、加入购物车和购买分别设置了权重0.0385、0.2375、0.3072和1,并采用皮尔森系数法计算用户之间的相似度,推荐结果与人们的经验是一致的。 相似文献
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基于社会标签的推荐系统研究 总被引：1，自引：0，他引：1
社会标签是一种新颖的大众索引方法,在Web 2.0时代各种收藏、检索、共享网站中得到广泛的应用,个性化推荐系统是基于用户的偏好为用户提供个性化信息服务的重要技术。本文针对推荐技术如何与社会标签结合的问题,分析了最新的研究现状和应用。详细阐述了3个方面的研究进展:标签推荐系统的研究、基于社会标签的个性化推荐系统的研究、社会标签的推荐应用系统。 相似文献
8.
【目的/意义】针对主流APPS推荐系统一般仅能推荐同类别APPS的现状,提出了一种基于协同过滤的
APPS跨类别推荐算法（APPSR）。【方法/过程】该算法先对APPS进行聚类,考虑APPS簇间相似度,对未评分APPS
进行评分预测,构建无缺失的用户-APPS评分矩阵。在传统协同过滤技术的基础之上,引入了时间权重函数与热
门APPS惩罚机制,体现了用户兴趣的时效性,消除了热门APPS对推荐结果的影响。根据不同用户对多种APPS的
评分,预测用户对其它类别APPS的喜好,为用户提供跨类别的APPS个性化推荐。【结果/结论】通过实验验证了
APPSR算法的可行性与有效性,为APPS个性化推荐提供了新方法。 相似文献
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为提高图书馆个性化推荐的效果,采用模糊聚类和模糊识别技术建立数字图书馆的个性化推荐系统。通过分析用户的信息素质、兴趣爱好、网络和电子资源检索情况,对读者进行数学模糊聚类分析,确定最佳阈值λ,得到最佳聚类。根据个体用户的基本情况进行模糊识别,由识别结果的归属给出针对当前用户的个性化推荐。实验结果表明,在模糊聚类与模糊识别基础上的个性化推荐方案是可行的和有效的,为创新数字图书馆个性化服务提供了一种新的方法。 相似文献
11.
《Information processing & management》2016,52(3):459-477
Nowadays, the increasing demand for group recommendations can be observed. In this paper we address the problem of recommendation performance for groups of users (group recommendation). We focus on the performance of very Top-N recommendations, which are important when recommending the long lasting items (only a few such items are consumed per session, e.g. movie). To improve existing group recommenders we propose a mixed hybrid recommender for groups combining content-based and collaborative strategies. The principle of proposed group recommender is to generate content and collaborative recommendations for each user, apply an aggregation strategy to solve the group conflict preferences for the content and collaborative sets separately, and finally reorder the collaborative candidates based on the content-based ones. It is based on an idea that candidates recommended by both recommendation strategies at the same time are presumably more appropriate for the group than the candidates recommended by individual strategies. The evaluation is performed by several experiments in the multimedia domain (as typical representative for group recommendations). Both, online and offline experiments were performed in order to compare real users’ satisfaction to the standard group recommenders and also, to compare performance of proposed approach to the state-of-the-art recommenders based on the MovieLens dataset. Finally, we experimented with the proposed hybrid recommender to generate the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation for groups of users and for the single-user recommendation respectively on very Top-N recommended items. 相似文献
12.
In collaborative filtering recommender systems recommendations can be made to groups of users. There are four basic stages in the collaborative filtering algorithms where the group’s users’ data can be aggregated to the data of the group of users: similarity metric, establishing the neighborhood, prediction phase, determination of recommended items. In this paper we perform aggregation experiments in each of the four stages and two fundamental conclusions are reached: (1) the system accuracy does not vary significantly according to the stage where the aggregation is performed, (2) the system performance improves notably when the aggregation is performed in an earlier stage of the collaborative filtering process. This paper provides a group recommendation similarity metric and demonstrates the convenience of tackling the aggregation of the group’s users in the actual similarity metric of the collaborative filtering process. 相似文献
13.
《Information processing & management》2023,60(4):103403
Most of the existing GNN-based recommender system models focus on learning users’ personalized preferences from these (explicit/implicit) positive feedback to achieve personalized recommendations. However, in the real-world recommender system, the users’ feedback behavior also includes negative feedback behavior (e.g., click dislike button), which also reflects users’ personalized preferences. How to utilize negative feedback is a challenging research problem. In this paper, we first qualitatively and quantitatively analyze the three kinds of negative feedback that widely existed in real-world recommender systems and investigate the role of negative feedback in recommender systems. We found that it is different from what we expected — not all negative items are ranked low, and some negative items are even ranked high in the overall items. Then, we propose a novel Signed Graph Neural Network Recommendation model (SiGRec) to encode the users’ negative feedback behavior. Our SiGRec can learn positive and negative embeddings of users and items via positive and negative graph neural network encoders, respectively. Besides, we also define a new Sign Cosine (SiC) loss function to adaptively mine the information of negative feedback for different types of negative feedback. Extensive experiments on four datasets demonstrate the proposed model outperforms several existing models. Specifically, on the Zhihu dataset, SiGRec outperforms the unsigned GNN model (i.e., LightGCN), 27.58% 29.81%, and 31.21% in P@20, R@20, and nDCG@20, respectively. We hope our work can open the door to further exploring the negative feedback in recommendations. 相似文献
14.
《Information processing & management》2023,60(5):103456
To achieve personalized recommendations, the recommender system selects the items that users may like by learning the collected user–item interaction data. However, the acquisition and use of data usually form a feedback loop, which leads to recommender systems suffering from popularity bias. To solve this problem, we propose a novel dual disentanglement of user–item interaction for recommendation with causal embedding (DDCE). Different from the existing work, our innovation is we take into account double-end popularity bias from the user-side and the item-side. Firstly, we perform a causal analysis of the reasons for user–item interaction and obtain the causal embedding representation of each part according to the analysis results. Secondly, on the item-side, we consider the influence of item attributes on popularity to improve the reliability of the item popularity. Then, on the user-side, we consider the effect of the time series when obtaining users’ interest. We model the contrastive learning task to disentangle users’ long–short-term interests, which avoids the bias of long–short-term interests overlapping, and use the attention mechanism to realize the dynamic integration of users’ long–short-term interests. Finally, we realize the disentanglement of user–item interaction reasons by decoupling user interest and item popularity. We experiment on two real-world datasets (Douban Movie and KuaiRec) to verify the significance of DDCE, the average improvement of DDCE in three evaluation metrics (NDCG, HR, and Recall) compared to the state-of-the-art model are 5.1106% and 4.1277% (MF as the backbone), 3.8256% and 3.2790% (LightGCN as the backbone), respectively. 相似文献
15.
Sajad Ahmadian Majid Meghdadi Mohsen Afsharchi 《Information processing & management》2018,54(4):707-725
Recommender systems are techniques to make personalized recommendations of items to users. In e-commerce sites and online sharing communities, providing high quality recommendations is an important issue which can help the users to make effective decisions to select a set of items. Collaborative filtering is an important type of the recommender systems that produces user specific recommendations of the items based on the patterns of ratings or usage (e.g. purchases). However, the quality of predicted ratings and neighbor selection for the users are important problems in the recommender systems. Selecting suitable neighbors set for the users leads to improve the accuracy of ratings prediction in recommendation process. In this paper, a novel social recommendation method is proposed which is based on an adaptive neighbor selection mechanism. In the proposed method first of all, initial neighbors set of the users is calculated using clustering algorithm. In this step, the combination of historical ratings and social information between the users are used to form initial neighbors set for the users. Then, these neighbor sets are used to predict initial ratings of the unseen items. Moreover, the quality of the initial predicted ratings is evaluated using a reliability measure which is based on the historical ratings and social information between the users. Then, a confidence model is proposed to remove useless users from the initial neighbors of the users and form a new adapted neighbors set for the users. Finally, new ratings of the unseen items are predicted using the new adapted neighbors set of the users and the interested items are recommended to the active user. Experimental results on three real-world datasets show that the proposed method significantly outperforms several state-of-the-art recommendation methods. 相似文献
16.
《Information processing & management》2020,57(2):102153
Social applications foster the involvement of end users in Web content creation, as a result of which a new source of vast amounts of data about users and their likes and dislikes has become available. Having access to users’ contributions to social sites and gaining insights into the consumers’ needs is of the utmost importance for marketing decision making in general, and to advertisement recommendation in particular. By analyzing this information, advertisement recommendation systems can attain a better understanding of the users’ interests and preferences, thus allowing these solutions to provide more precise ad suggestions. However, in addition to the already complex challenges that hamper the performance of recommender systems (i.e., data sparsity, cold-start, diversity, accuracy and scalability), new issues that should be considered have also emerged from the need to deal with heterogeneous data gathered from disparate sources. The technologies surrounding Linked Data and the Semantic Web have proved effective for knowledge management and data integration. In this work, an ontology-based advertisement recommendation system that leverages the data produced by users in social networking sites is proposed, and this approach is substantiated by a shared ontology model with which to represent both users’ profiles and the content of advertisements. Both users and advertisement are represented by means of vectors generated using natural language processing techniques, which collect ontological entities from textual content. The ad recommender framework has been extensively validated in a simulated environment, obtaining an aggregated f-measure of 79.2% and a Mean Average Precision at 3 (MAP@3) of 85.6%. 相似文献
17.
A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individual’s capability to survey. Music recommendation is considered a popular application area. In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focus on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to utilize information extracted directly from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. In addition, this model has been extended for improved recommendation performance by utilizing audio features that help alleviate three well-known problems associated with data sparseness in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experimental results based on two real-world data sets lead us to believe that content information is crucial in achieving better personalized recommendation beyond user ratings. We further show how primitive audio features can be combined into aggregate features for the proposed CRMS and analyze their influences on recommendation performance. Although this model was developed originally for music collaborative recommendation based on audio features, our experiment with the movie data set demonstrates that it can be applied to other domains. 相似文献
18.
《Information processing & management》2022,59(5):103031
Online recommender systems have been shown to be vulnerable to group shilling attacks in which attackers of a shilling group collaboratively inject fake profiles with the aim of increasing or decreasing the frequency that particular items are recommended. Existing detection methods mainly use the frequent itemset (dense subgraph) mining or clustering method to generate candidate groups and then utilize the hand-crafted features to identify shilling groups. However, such two-stage detection methods have two limitations. On the one hand, due to the sensitivity of support threshold or clustering parameters setting, it is difficult to guarantee the quality of candidate groups generated. On the other hand, they all rely on manual feature engineering to extract detection features, which is costly and time-consuming. To address these two limitations, we present a shilling group detection method based on graph convolutional network. First, we model the given dataset as a graph by treating users as nodes and co-rating relations between users as edges. By assigning edge weights and filtering normal user relations, we obtain the suspicious user relation graph. Second, we use principal component analysis to refine the rating features of users and obtain the user feature matrix. Third, we design a three-layer graph convolutional network model with a neighbor filtering mechanism and perform user classification by combining both structure and rating features of users. Finally, we detect shilling groups through identifying target items rated by the attackers according to the user classification results. Extensive experiments show that the classification accuracy and detection performance (F1-measure) of the proposed method can reach 98.92% and 99.92% on the Netflix dataset and 93.18% and 92.41% on the Amazon dataset. 相似文献
19.
[目的/意义]旨在探究信息内容特征对移动医疗APP用户推荐行为的影响和作用路径，提炼出对运营和管理具有积极意义的意见和方法。[方法/过程]以精细加工可能性模型为理论框架，通过整合信息系统成功模型和口碑营销经典理论的相关因素，构建出"中心信息内容特征——自我推荐"以及"边缘信息内容特征——从众推荐"两条作用路径，并聚焦感知需求的调节作用提出假设并构建研究模型。实证研究采用"日志追踪实验+调查问卷"的方式收集来自移动医疗APP用户的288份数据，利用SmartPLS2.0对模型进行检验。[结果/结论]信息质量、服务质量、系统质量正向影响自我推荐意愿；APP认知声誉、医生认知声誉、医院认知声誉正向影响从众推荐意愿；感知需求正向调节自我推荐意愿对推荐行为的影响，而从众推荐意愿对推荐行为的影响不具有显著调节作用。 相似文献
20.
《Information processing & management》2023,60(3):103273
The domain about what it means to give responsible and human centric recommendations in the context of Artificial Intelligence (AI)-based insurance has not yet been fully explored. In this article, we therefore, first provide an in-depth analysis and perform a systematic literature review on (i) the specifications and requirements for such systems from a regulation point of view, (ii) instructions on which data they can rely upon, (iii) which recommender techniques can be used for developing such an advisor, (iv) off-the-shelf components for the trustworthy, responsible, and ethical behavior of this AI-empowered tool. Then, we present a novel approach, based on AI, to suggest insurance coverage for users and families, as well as instructions on how to design such a system. The solution, as proposed in our paper, will be transparent, trustworthy, and responsible to the final users and thus, hopefully, better accepted by customers. After describing a possible system design and architecture, we critically discuss the challenges and opportunities for the deployment of such systems in insurance companies. 相似文献