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1.
《Information processing & management》2023,60(4):103377
The recent boom in online courses has necessitated personalized online course recommendation. Modelling the learning sequences of users is key for course recommendation because the sequences contain the dynamic learning interests of the users. However, current course recommendation methods ignore heterogeneous course information and collective sequential dependency between courses when modelling the learning sequences. We thus propose a novel online course recommendation method based on knowledge graph and deep learning which models course information via a course knowledge graph and represents courses using TransD. It then develops a bidirectional long short-term memory network, convolutional neural network, and multi-layer perceptron for learning sequence modelling and course recommendation. A public dataset called MOOCCube was used to evaluate the proposed method. Experimental results show that: (1) employing the course knowledge graph in learning sequence modelling improves averagely the performance of our method by 13.658%, 16.42%, and 15.39% in terms of HR@K, MRR@K, and NDCG@K; (2) modelling the collective sequential dependency improves averagely the performance by 4.11%, 6.37%, and 5.47% in terms of the above metrics; and (3) our method outperforms popular methods with the course knowledge graph in most cases. 相似文献
2.
针对创新社区日益增长的海量信息阻碍了用户对知识进行有效获取和创造的现状,将模糊形式概念分析（FFCA）理论应用于创新社区领先用户的个性化知识推荐研究。首先识别出创新社区领先用户并对其发帖内容进行文本挖掘得到用户——知识模糊形式背景,然后构建带有相似度的模糊概念格对用户偏好进行建模,最后基于模糊概念格和协同过滤的推荐算法为领先用户提供个性化知识推荐有序列表。以手机用户创新社区为例,验证了基于FFCA的领先用户个性化知识推荐方法的可行性,有助于满足用户个性化知识需求,促进用户更好地参与社区知识创新。 相似文献
3.
《Information processing & management》2020,57(2):102151
Learning latent representations for users and points of interests (POIs) is an important task in location-based social networks (LBSN), which could largely benefit multiple location-based services, such as POI recommendation and social link prediction. Many contextual factors, like geographical influence, user social relationship and temporal information, are available in LBSN and would be useful for this task. However, incorporating all these contextual factors for user and POI representation learning in LBSN remains challenging, due to their heterogeneous nature. Although the encouraging performance of POI recommendation and social link prediction are delivered, most of the existing representation learning methods for LBSN incorporate only one or two of these contextual factors. In this paper, we propose a novel joint representation learning framework for users and POIs in LBSN, named UP2VEC. In UP2VEC, we present a heterogeneous LBSN graph to incorporate all these aforementioned factors. Specifically, the transition probabilities between nodes inside the heterogeneous graph are derived by jointly considering these contextual factors. The latent representations of users and POIs are then learnt by matching the topological structure of the heterogeneous graph. For evaluating the effectiveness of UP2VEC, a series of experiments are conducted with two real-world datasets (Foursquare and Gowalla) in terms of POI recommendation and social link prediction. Experimental results demonstrate that the proposed UP2VEC significantly outperforms the existing state-of-the-art alternatives. Further experiment shows the superiority of UP2VEC in handling cold-start problem for POI recommendation. 相似文献
4.
【目的/意义】为揭示社会化问答情境下用户知识内化过程和内在动因,提升社会化问答社区知识的利用率
和重用率。【方法/过程】本研究基于同化顺应理论、信息加工学习理论构建了社会化问答情境下用户知识内化过程
模型,分析其作用过程和机理。然后,从组态视角运用模糊集定性比较分析（fsQCA）方法分析了用户知识内化的动
因和影响路径。【结果/结论】研究发现：社会化问答情境下用户的知识内化是以用户已有的认知结构为制约机制,
知识经过同化和顺应后被元认知监控,然后由知识反馈调节,同时受平台传播能力和用户自身吸收能力两个主要
因素影响。促成社会化问答情境下用户知识内化发生的条件组态路径有用户促进型和平台促进型两类;抑制社会
化问答情境下用户知识内化发生的条件组态路径有用户抑制型和平台抑制型两类。【创新/局限】不同类型和场景
下社会化问答社区用户的知识内化差异化动因还需进一步研究。 相似文献
5.
同侪影响对用户贡献行为的作用研究———基于网络客观大数据的分析 《科学学研究》2021,39(12):2294-2304
本文将同侪影响引入在线创新社区的用户行为研究中,从广度和深度两方面考察同侪影响对用户贡献行为的影响,并分析感知收益的中介作用。研究以小米社区MIUI功能与讨论区的创意集市板块为对象构建S-O-R模型,采用6567名用户发布的8830条创意、5.26万条评论和收到的103.36万条评论数据,利用Mplus8.1分析检验,结果发现：同侪影响广度与深度均有利于促进用户贡献行为,综合收益在同侪影响广度、深度与用户贡献行为间起正向中介效应,情感收益仅在同侪影响广度、深度与主动贡献行为间起正向中介效应,而认知收益则在同侪影响深度与反应贡献行为间起负向中介效应。研究拓展了在线网络情境下知识管理与社会学领域的交叉研究,并为在线创新社区社交网络和知识管理提供重要启示。 相似文献
6.
《Information processing & management》2020,57(2):102056
Identifying and extracting user communities is an important step towards understanding social network dynamics from a macro perspective. For this reason, the work in this paper explores various aspects related to the identification of user communities. To date, user community detection methods employ either explicit links between users (link analysis), or users’ topics of interest in posted content (content analysis), or in tandem. Little work has considered temporal evolution when identifying user communities in a way to group together those users who share not only similar topical interests but also similar temporal behavior towards their topics of interest. In this paper, we identify user communities through multimodal feature learning (embeddings). Our core contributions can be enumerated as (a) we propose a new method for learning neural embeddings for users based on their temporal content similarity; (b) we learn user embeddings based on their social network connections (links) through neural graph embeddings; (c) we systematically interpolate temporal content-based embeddings and social link-based embeddings to capture both social network connections and temporal content evolution for representing users, and (d) we systematically evaluate the quality of each embedding type in isolation and also when interpolated together and demonstrate their performance on a Twitter dataset under two different application scenarios, namely news recommendation and user prediction. We find that (1) content-based methods produce higher quality communities compared to link-based methods; (2) methods that consider temporal evolution of content, our proposed method in particular, show better performance compared to their non-temporal counter-parts; (3) communities that are produced when time is explicitly incorporated in user vector representations have higher quality than the ones produced when time is incorporated into a generative process, and finally (4) while link-based methods are weaker than content-based methods, their interpolation with content-based methods leads to improved quality of the identified communities. 相似文献
7.
[目的/意义]学术用户画像是对用户访问使用学术资源行为的较全面的刻画。本文尝试构建图书馆学术用户画像的信息行为标签和研究兴趣标签,来准确定位学术用户的信息需求,以便推荐合适的学术资源。[方法/过程]具体方法是全面获取用户的访问日志并进行清洗处理,然后构建从学术用户信息行为出发的用户画像标签体系,进一步研究构建了基于研究兴趣关联的信息资源推荐服务。[结果/结论]本研究有助于提高用户信息获取效率,提高图书馆学术资源推荐服务的质量,并为结合其它资源全面构建图书馆学术用户画像提供一定的借鉴。 相似文献
8.
《Information processing & management》2020,57(2):102169
Whether to deal with issues related to information ranking (e.g. search engines) or content recommendation (on social networks, for instance), algorithms are at the core of processes that select which information is made visible. Such algorithmic choices have a strong impact on users’ activity de facto, and therefore on their access to information. This raises the question of how to measure the quality of the choices algorithms make and their impact on users. As a first step in that direction, this paper presents a framework with which to analyze the diversity of information accessed by users in the context of musical content.The approach adopted centers on the representation of user activity through a tripartite graph that maps users to products and products to categories. In turn, conducting random walks in this structure makes it possible to analyze how categories catch users’ attention and how this attention is distributed. Building upon this distribution, we propose a new index referred to as the (calibrated) herfindahl diversity, which is aimed at quantifying the extent to which this distribution is diverse and representative of existing categories.To the best of our knowledge, this paper is the first to connect the output of random walks on graphs with diversity indexes. We demonstrate the benefit of such an approach by applying our index to two datasets that record user activity on online platforms involving musical content. The results are threefold. First, we show that our index can discriminate between different user behaviors. Second, we shed some light on a saturation phenomenon in the diversity of users’ attention. Finally, we show that the lack of diversity observed in the datasets derives from exogenous factors related to the heterogeneous popularity of music styles, as opposed to internal factors such as recurrent user behaviors. 相似文献
9.
This article presents conceptual navigation and NavCon, an architecture that implements this navigation in World Wide Web pages. NavCon architecture makes use of ontology as metadata to contextualize user search for information. Based on ontologies, NavCon automatically inserts conceptual links in Web pages. By using these links, the user may navigate in a graph representing ontology concepts and their relationships. By browsing this graph, it is possible to reach documents associated with the user desired ontology concept. This Web navigation supported by ontology concepts we call conceptual navigation. Conceptual navigation is a technique to browse Web sites within a context. The context filters relevant retrieved information. The context also drives user navigation through paths that meet his needs. A company may implement conceptual navigation to improve user search for information in a knowledge management environment. We suggest that the use of an ontology to conduct navigation in an Intranet may help the user to have a better understanding about the knowledge structure of the company. 相似文献
10.
《Information processing & management》2020,57(2):102142
With the information explosion of news articles, personalized news recommendation has become important for users to quickly find news that they are interested in. Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods which characterize the content of user reading history. Although these methods have achieved good performances, they still suffer from data sparse problem, since most of them fail to extensively exploit high-order structure information (similar users tend to read similar news articles) in news recommendation systems. In this paper, we propose to build a heterogeneous graph to explicitly model the interactions among users, news and latent topics. The incorporated topic information would help indicate a user’s interest and alleviate the sparsity of user-item interactions. Then we take advantage of graph neural networks to learn user and news representations that encode high-order structure information by propagating embeddings over the graph. The learned user embeddings with complete historic user clicks capture the users’ long-term interests. We also consider a user’s short-term interest using the recent reading history with an attention based LSTM model. Experimental results on real-world datasets show that our proposed model significantly outperforms state-of-the-art methods on news recommendation. 相似文献
11.
《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
12.
《International Journal of Information Management》2016,36(4):520-530
The research on users as a source of innovation has been coming into blossom and the studies about the effect of users’ lead userness on their innovation-related activities are drawing more and more attention from both academic and business circles. However, there have been few empirical studies exploring the relationship between users’ lead userness and their innovation-related knowledge sharing behavior in the context of online user community and the mediating effects of users’ social capital and their perceived behavioral control on this relationship. By empirically analyzing the 140 data collected from an online user community that is used as an important source of innovation for a company with the structural equation modeling analysis through the partial least squares method, this study reveals that users’ lead userness has a positive relationship with their innovation-related knowledge sharing in the online user community and users’ social capital and perceived behavioral control jointly and fully mediate this positive relationship. Based on the new findings, this study is expected to provide useful implications which can contribute to widening and deepening the research stream about the effect of users’ lead userness on their innovation-related knowledge sharing in the online user community. 相似文献
13.
[目的/意义]在社会化标注系统自组织运行的基础上，构建个性化信息推荐的多维度融合与优化模型，进而在大数据环境下，为用户提供精准的个性化信息推荐服务，从而进一步丰富个性化信息推荐的理论体系以及拓展个性化信息推荐的研究方法。[方法/过程]首先，对每一种个性化信息推荐方法的优点和不足进行深入分析；然后，将基于图论（社会网络关系）、基于协同过滤以及基于内容（主题）3种个性化信息推荐方法进行多维度深度融合，构建个性化信息推荐多维度融合模型；最后，对社会化标注系统中个性化信息推荐多维度融合模型进行优化，从而解决个性化推荐过程中用户"冷启动"、数据稀疏性和用户偏好漂移等问题。[结果/结论]通过综合考虑现有的基于图论（社会网络关系）、基于协同过滤以及基于内容（主题）的个性化信息推荐方法各自的贡献和不足，实现3种方法之间的多维度深度融合，并结合心理认知、用户情境以及时间、空间等优化因素，最终构建出社会化标注系统中个性化信息推荐多维度融合与优化模型。 相似文献
14.
随着互联网的发展,激发虚拟品牌社区中有利于企业的知识创新,成为企业应对动态市场环境而实施创新战略的关键。本文基于C2C与B2C虚拟品牌社区中用户探索性/应用性学习和用户创意组合管理的视角,构建了激发有利于企业知识创新的理论模型。实证发现:C2C与B2C虚拟品牌社区会对用户探索性/应用性学习方式产生差异影响,相对于B2C虚拟品牌社区有利于用户的应用性学习,C2C虚拟品牌社区倾向于用户的探索性学习,而探索性学习更有利于激发用户知识创新能力;进一步,用户知识创新能力对激发用户产生有利于企业知识创新的作用并不显著;但是,用户创意组合管理在用户知识创新能力对激发有利于企业知识创新的关系中起到显著的正向调节作用。本研究为企业实施用户知识管理创新战略提出了有价值的启示。 相似文献
15.
[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。 相似文献
16.
[目的/意义] 知识增殖已成为判别在线知识社区成功与否的具体标准。将Web2.0技术所支持的用户行为视作知识增殖的载体,将社区规则、规范所鼓励的人际信任视作知识增殖的条件,考察用户行为与用户信任的互惠因果关系,探索在线社区知识增殖规律。[方法/过程] 采用问卷调查法获取332份有效样本,运用AMOS20.0软件检验模型假设。[结果/结论] 用户行为的发展过程为"知识搜寻行为—知识应用行为—知识贡献行为";用户间信任的发展过程为"认知信任—情感信任",用户行为和用户信任互为因果、二者相互促进实现在线社区知识增殖。 相似文献
17.
[目的/意义]探索基于知识图谱的网络社区学术资源深度聚合的理论和方法,为网络学术社区知识细粒度组织、知识服务实践提供思路引导和新视角。[方法/过程]首先梳理了知识图谱和学术资源聚合的研究进展,从价值需求主体的角度剖析网络社区学术资源聚合的应用价值;然后明确网络社区学术知识图谱的构建流程,构建出基于知识图谱的网络社区学术资源深度聚合框架,并介绍知识富关联关系提取方法;最后设计个体用户画像、语义智能检索、分面式导航三种应用模式。[结果/结论]知识图谱能够较好地应用于学术资源深度聚合,支持网络社区的高级知识服务应用,基于知识图谱的网络社区学术资源深度聚合框架对学术类网络社区平台优化资源配置、有效知识创新服务具有重要参考价值。 相似文献
18.
【目的/意义】为了向在线医疗社区中的用户自动推荐符合其自身实际需求的医生,本文基于在线问诊文本
信息,提出了基于相似用户与相似医生的混合医生推荐算法。【方法/过程】首先从用户咨询问题出发,找到具有相
似咨询问题的用户,将其所选择的医生作为基于相似用户的推荐集合;然后从医生回答从发,通过LDA主题模型训
练,从医生回答文本集中挖掘出隐含的疾病主题,按主题查找具有相似疾病诊治经验的医生作为推荐集合;最后通
过混合相似度计算融合基于相似用户和相似医生的推荐结果,得到最终推荐列表。【结果/结论】通过对在线医疗社
区“39健康网”进行实证研究,结果表明,利用本文提出的方法进行推荐,能够有效降低数据维度,挖掘文本间的潜
在语义关联,有效缩小语义鸿沟,提升推荐质量,具有较好的推荐效果。【创新/局限】本文仅选取了针对科室的小样
本数据进行实验,且部分参数使用经验值,未来可深入探讨该方法在大规模医疗数据集上的应用。 相似文献
19.
In online travel communities, ‘Top-K best places to visit’ recommendations are gaining more attention from travelers due to their ubiquitous access to the Internet, but little empirical effort has been made to investigate what factors lead to the popularity of user-curated ‘best places to visit (BP2V)’ recommendations. This research therefore aims to identify and validate the heuristic factors affecting the popularity of BP2V recommendations. Based on the heuristic-systematic model (HSM) of persuasion, we derive recommender-related (i.e., recommender's identity disclosure, reputation, experience, and location of residency) and recommendation-related (i.e., number of places recommended, helpfulness rating, number of comments added, and length of recommendation) heuristic characteristics of BP2V recommendations and investigate their impact on recommendation popularity. In addition, this study examines the moderating effect of destination category (i.e., attractions, food, shopping, and activities) on the relationship between heuristic characteristics and the popularity of BP2V recommendations. Our empirical results, which were based on 565 ‘best places to visit in the U.S.’ recommendation postings from Qyer.com, a major online travel community in China, suggest that recommender's identity disclosure, reputation, number of places recommended, helpfulness rating, and length of recommendation are positively associated with recommendation popularity. We also found that the relationships between heuristic factors and the popularity of BP2V recommendations are contingent on destination category. This study will contribute to the body of knowledge on online travel communities and HSM and provide valuable implications for general travelers and managers in the tourism and hospitality industry. 相似文献
20.
《Information processing & management》2023,60(4):103402
In community-based social media, users consume content from multiple communities and provide feedback. The community-related data reflect user interests, but they are poorly used as additional information to enrich user-content interaction for content recommendation in existing studies. This paper employs an information seeking behavior perspective to describe user content consumption behavior in community-based social media, therefore revealing the relations between user, community and content. Based on that, the paper proposes a Community-aware Information Seeking based Content Recommender (abbreviated as CISCRec) to use the relations for better modeling user preferences on content and increase the reasoning on the recommendation results. CISCRec includes two key components: a two-level TransE prediction framework and interaction-aware embedding enhancement. The two-level TransE prediction framework hierarchically models users’ preferences for content by considering community entities based on the TransE method. Interaction-aware embedding enhancement is designed based on the analysis of users’ continued engagement in online communities, aiming to add expressiveness to embeddings in the prediction framework. To verify the effectiveness of the model, the real-world Reddit dataset (4,868 users, 115,491 contents, 850 communities, and 602,025 interactions) is chosen for evaluation. The results show that CISCRec outperforms 8 common baselines by 9.33%, 4.71%, 42.13%, and 14.36% on average under the Precision, Recall, MRR, and NDCG respectively. 相似文献