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《Information processing & management》2019,56(3):526-540
Recommender Systems deal with the issue of overloading information by retrieving the most relevant sources in the wide range of web services. They help users by predicting their interests in many domains like e-government, social networks, e-commerce and entertainment. Collaborative Filtering (CF) is the most promising technique used in recommender systems to give suggestions based on liked-mind users’ preferences. Despite the widespread use of CF in providing personalized recommendation, this technique has problems including cold start, data sparsity and gray sheep. Eventually, these problems lead to the deterioration of the efficiency of CF. Most existing recommendation methods have been proposed to overcome the problems of CF. However, they fail to suggest the top-n recommendations based on the sequencing of the users’ priorities. In this research, to overcome the shortcomings of CF and current recommendation methods in ranking preference dataset, we have used a new graph-based structure to model the users’ priorities and capture the association between users and items. Users’ profiles are created based on their past and current interest. This is done because their interest can change with time. Our proposed algorithm keeps the preferred items of active user at the beginning of the recommendation list. This means these items come under top-n recommendations, which results in satisfaction among users. The experimental results demonstrate that our algorithm archives the significant improvement in comparison with CF and other proposed recommendation methods in terms of recall, precision, f-measure and MAP metrics using two benchmark datasets including MovieLens and Superstore. 相似文献
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Graph-based recommendation approaches use a graph model to represent the relationships between users and items, and exploit the graph structure to make recommendations. Recent graph-based recommendation approaches focused on capturing users’ pairwise preferences and utilized a graph model to exploit the relationships between different entities in the graph. In this paper, we focus on the impact of pairwise preferences on the diversity of recommendations. We propose a novel graph-based ranking oriented recommendation algorithm that exploits both explicit and implicit feedback of users. The algorithm utilizes a user-preference-item tripartite graph model and modified resource allocation process to match the target user with users who share similar preferences, and make personalized recommendations. The principle of the additional preference layer is to capture users’ pairwise preferences, provide detailed information of users for further recommendations. Empirical analysis of four benchmark datasets demonstrated that our proposed algorithm performs better in most situations than other graph-based and ranking-oriented benchmark algorithms. 相似文献
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我国高校文库网络化现状的调查分析 总被引：8，自引：1，他引：7
对全国680所高校图书馆进行了高校文库网络化现状的调查.结果表明:有96个图书馆建立了高校文库,56个图书馆在其主页上设有文库栏目,41个网络文库能登录访问.针对网络文库现存的问题,提出了建立与优化网络文库的建议. 相似文献
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People are gregarious by nature, which explains why group activities, from colleagues sharing a meal to friends attending a book club event together, are the social norm. Online group recommenders identify items of interest, such as restaurants, movies, and books, that satisfy the collective needs of a group (rather than the interests of individual group members). With a number of new movies being released every week, online recommenders play a significant role in suggesting movies for family members or groups of friends/people to watch, either at home or at movie theaters. Making group recommendations relevant to the joint interests of a group, however, is not a trivial task due to the diversity in preferences among group members. To address this issue, we introduce GroupReM which makes movie recommendations appealing (to a certain degree) to members of a group by (i) employing a merging strategy to explore individual group members’ interests in movies and create a profile that reflects the preferences of the group on movies, (ii) using word-correlation factors to find movies similar in content, and (iii) considering the popularity of movies at a movie website. Unlike existing group recommenders based on collaborative filtering (CF) which consider ratings of movies to perform the recommendation task, GroupReM primarily employs (personal) tags for capturing the contents of movies considered for recommendation and group members’ interests. The design of GroupReM, which is simple and domain-independent, can easily be extended to make group recommendations on items other than movies. Empirical studies conducted using more than 3000 groups of different users in the MovieLens dataset, which are various in terms of numbers and preferences in movies, show that GroupReM is highly effective and efficient in recommending movies appealing to a group. Experimental results also verify that GroupReM outperforms popular CF-based recommenders in making group recommendations. 相似文献
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《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
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机构库资源是开放存取资源的重要组成部分,是用户网上获取免费资源的重要途径之一。本文从用户利用的角度出发,从机构库的注册站点、机构库网站、机构库联盟、搜索引擎等几个方面探讨了机构库资源的获取策略,以期为用户利用机构库资源提供指南。 相似文献
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本文以国内主要的大学机构知识库为调研对象,对大学机构知识库的建设和运行中存在的问题和弊端进行了深刻的分析和探讨。文章认为区域联盟工程应用型高校机构知识库应该突出机构资源实践应用的特色,组织模式体现区域联盟特色,是建设和管理机构知识库的关键点。本文探讨了区域联盟体内高校和其它机构之间联合建设机构知识库的构建模式、组织机制、机构资源的甄选、机构库平台的建设、宣传与推广等内容。 相似文献
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Sampsa Hyysalo 《Research Policy》2009,38(5):726-735
Failure to meet the preferences and needs of users has been consistently stressed as a major cause of unsuccessful R&D for over 30 years. Yet little seems to change. An important element in this “producer-user paradox” is a lack of frameworks able to inform empirical research and the work that people do when they bridge designing, implementing, using and managing new technology. “Learning economy” and “social learning in technological innovation” appear promising as such integrative frameworks not least due to their emphasis on learning between producers and users. The present paper examines the value in the way learning is treated in these frameworks for empirical research and for the practitioners, and to this aim contrasts these frameworks to findings from a line of studies on learning between producers and users of new health technologies. 相似文献
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针对我国机构库建设落后的现状,探讨现有的机构库建设模式及我国机构库建设的需求及障碍,在分析Wordpress系统的适应性的基础上,提供了一个基于Wordpress的中小单位机构库解决方案是：基于Wordpress的机构库通用系统的集成、细节需求的深度定制、融合搜索引擎的资源推广策略,并分别阐述了实现上述三部分的措施和建议。 相似文献
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[目的/意义]在现有社交网络用户自我披露领域的研究中,出现了大量混合性的实验结论。同时,当前学界针对用户披露内容的挖掘尚浅,存在角度单一、划分离散且不全面、社交网络媒介特性缺失等问题。[方法/过程]通过引入自我差异理论,挖掘社交网络用户自我披露的内容特征。以社交网络用户群体为研究对象,通过问卷调查法,基于筛选获得的344份问卷,采用多元逻辑回归等方法进行分析。[结果/结论]隐私、信任、社会资本因素对用户社交网络披露不同自我维度的频率及偏好均存在不同程度的影响。新角度的引入加深了对社交网络用户自我披露的理解,也为后续研究的展开提供新的思路。 相似文献
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There is a growing body of literature calling for work on the emerging role of smart cities as information hubs and knowledge repositories. This article reviews the existing smart city literature and integrates knowledge management perspectives to provide an overview of future research directions. By demonstrating the multi-stakeholder relationships involved in smart city development, it takes a crucial step towards looking into the role of knowledge management in future smart city research. Eighty-two peer-reviewed publications were analyzed covering smart city studies in various research domains. The systematic review identifies five different themes: strategy and vision, frameworks, enablers and inhibitors, citizen participation, and benefits. These themes form the basis for developing a future research agenda focused on knowledge sharing and co-learning among cities via three research directions: socio-technical approaches, knowledge sharing perspectives and organizational learning capabilities. The paper also proposes a series of knowledge-driven policy recommendations to contribute towards the UN Sustainable Development Goals. 相似文献
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《Information processing & management》2014,50(4):599-620
Query enrichment is a process of dynamically enhancing a user query based on her preferences and context in order to provide a personalized answer. The central idea is that different users may find different services relevant due to different preferences and contexts. In this paper, we present a preference model that combines user preferences, user context, domain knowledge to enrich the initial user query. We use CP-nets to rank the preferences using implicit and explicit user preferences and domain knowledge. We present some algorithms for preferential matching. We have implemented the proposed model as a prototype. The initial results look promising. 相似文献
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国内外机构库现状调查及建设 总被引：2，自引：0，他引：2
机构库是用来保存并使用机构研究成果的数据库,本文通过网络调查,对目前国内外机构库的应用软件、收录资料的数量、类型等进行了分析研究,并对国内的大学或研究机构如何着手规划建置机构库提出一些建议。 相似文献
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《Information processing & management》2016,52(4):682-697
Object matching is an important task for finding the correspondence between objects in different domains, such as documents in different languages and users in different databases. In this paper, we propose probabilistic latent variable models that offer many-to-many matching without correspondence information or similarity measures between different domains. The proposed model assumes that there is an infinite number of latent vectors that are shared by all domains, and that each object is generated from one of the latent vectors and a domain-specific projection. By inferring the latent vector used for generating each object, objects in different domains are clustered according to the vectors that they share. Thus, we can realize matching between groups of objects in different domains in an unsupervised manner. We give learning procedures of the proposed model based on a stochastic EM algorithm. We also derive learning procedures in a semi-supervised setting, where correspondence information for some objects are given. The effectiveness of the proposed models is demonstrated by experiments on synthetic and real data sets. 相似文献
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《Information processing & management》2022,59(2):102821
People often search for information in order to learn something new. In recent years, the “search-as-learning” movement has argued that search systems should be better designed to support learning. Current search systems (especially Web search engines) are largely designed and optimized to fulfill simple look-up tasks (e.g., navigational or fact-finding search tasks). However, they provide less support for searchers working on complex tasks that involve learning. Search-as-learning studies have investigated a wide range of research questions. For example, studies have aimed to better understand how characteristics of the individual searcher, the type of search task, and interactive features provided by the system can influence learning outcomes. Learning assessment is a key component in search-as-learning studies. Assessment materials are used to both gauge prior knowledge and measure learning during or after one or more search sessions. In this paper, we provide a systematic review of different types of assessments used in search-as-learning studies to date. The paper makes the following three contributions. First, we review different types of assessments used and discuss their potential benefits and drawbacks. Second, we review assessments used outside of search-as-learning, which may provide insights and opportunities for future research. Third, we provide recommendations for future research. Importantly, we argue that future studies should clearly define learning objectives and develop assessment materials that reliably capture the intended type of learning. For example, assessment materials should test a participant’s ability to engage with specific cognitive processes, which may range from simple (e.g., memorization) to more complex (e.g., critical and creative thinking). Additionally, we argue that future studies should consider two dimensions that are understudied in search-as-learning: long-term retention (i.e., being able to use what was learned in the long term) and transfer of learning (i.e., being able to use what was learned in a novel context). 相似文献
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现有机构知识库服务难以满足科研用户的知识需求,应增强知识管理在机构知识库中的作用,并在此基础上构建基于知识管理的机构知识库服务体系,可有力提升机构知识库的服务水平,使机构知识库快速地从信息服务向知识服务迈进。 相似文献
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《Information processing & management》2020,57(1):102122
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions.In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis. 相似文献