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1.
Fattane Zarrinkalam Mohsen Kahani Ebrahim Bagheri 《Information processing & management》2018,54(2):339-357
Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users. 相似文献
2.
《Information processing & management》2020,57(6):102353
In this paper, we focus on the problem of discovering internally connected communities in event-based social networks (EBSNs) and propose a community detection method by utilizing social influences between users. Different from traditional social network, EBSNs contain different types of entities and links, and users in EBSNs have more complex behaviours. This leads to poor performance of the traditional social influence computation method in EBSNs. Therefore, to quantify the pairwise social influence accurately in EBSNs, we first propose to compute two types of social influences, i.e., structure-based social influence and behaviour-based social influence, by utilizing the online social network structure and offline social behaviours of users. In particular, based on the specific features of EBSNs, the similarities of user preference on three aspects (i.e., topics, regions and organizers) are utilized to measure the behaviour-based social influence. Then, we obtain the unified pairwise social influence by combining these two types of social influences through a weight function. Next, we present a social influence based community detection algorithm which is referred to as SICD. In SICD, inspired by the nonlinear feature learning ability of the autoencoder, we first devise a neighborhood based deep autoencoder algorithm to obtain nonlinear community-oriented latent representations of users, and then utilize the k-means algorithm for community detection. Experimental results conducted on real-world dataset show the effectiveness of our proposed algorithm. 相似文献
3.
《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
4.
《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
5.
【 目的/意义】研究从用户群体的角度出发,依据用户特征对社区用户进行群体划分,以了解不同用户群体的
主题差异,从而更加全面清晰的了解社区主题,更好的为社区用户推荐资源。【方法/过程】研究利用社会网络分析
和Topsis算法对用户群体进行划分,再利用LDA模型分别对不同用户进行主题挖掘,最后采用谱聚类实现主题优
化。【结果/结论】科学网情报学社区的核心用户与一般用户群体主题有相同的部分,也存在差异,核心用户群体的
主题专指性较强,一般用户群体的主题较为广泛。基于虚拟学术社区用户群体主题挖掘模型,可以更加全面展示
社区用户关注的主题,更好地为社区用户推荐资源。【创新/局限】研究从用户群体的视角出发,提出了虚拟学术社
区用户群体主题挖掘模型,更好的为社区用户推荐资源,但本研究在数据量、主题模型以及社会网络分析指标的选
取等方面还需要拓展与延伸。 相似文献
6.
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
7.
《Information processing & management》2022,59(3):102936
Session-based recommendation aims to predict items that a user will interact with based on historical behaviors in anonymous sessions. It has long faced two challenges: (1) the dynamic change of user intents which makes user preferences towards items change over time; (2) the uncertainty of user behaviors which adds noise to hinder precise preference learning. They jointly preclude recommender system from capturing real intents of users. Existing methods have not properly solved these problems since they either ignore many useful factors like the temporal information when building item embeddings, or do not explicitly filter out noisy clicks in sessions. To tackle above issues, we propose a novel Dynamic Intent-aware Iterative Denoising Network (DIDN) for session-based recommendation. Specifically, to model the dynamic intents of users, we present a dynamic intent-aware module that incorporates item-aware, user-aware and temporal-aware information to learn dynamic item embeddings. A novel iterative denoising module is then devised to explicitly filter out noisy clicks within a session. In addition, we mine collaborative information to further enrich the session semantics. Extensive experimental results on three real-world datasets demonstrate the effectiveness of the proposed DIDN. Specifically, DIDN obtains improvements over the best baselines by 1.66%, 1.75%, and 7.76% in terms of P@20 and 1.70%, 2.20%, and 10.48% in terms of MRR@20 on all datasets. 相似文献
8.
Mining direct antagonistic communities in signed social networks 总被引：1，自引：1，他引：0
David Lo Didi Surian Philips Kokoh Prasetyo Kuan Zhang Ee-Peng Lim 《Information processing & management》2013
Social networks provide a wealth of data to study relationship dynamics among people. Most social networks such as Epinions and Facebook allow users to declare trusts or friendships with other users. Some of them also allow users to declare distrusts or negative relationships. When both positive and negative links co-exist in a network, some interesting community structures can be studied. In this work, we mine Direct Antagonistic Communities (DACs) within such signed networks. Each DAC consists of two sub-communities with positive relationships among members of each sub-community, and negative relationships among members of the other sub-community. Identifying direct antagonistic communities is an important step to understand the nature of the formation, dissolution, and evolution of such communities. Knowledge about antagonistic communities allows us to better understand and explain behaviors of users in the communities. 相似文献
9.
《Information processing & management》2022,59(1):102757
WhatsApp emerged as a major communication platform in many countries in the recent years. Despite offering only one-to-one and small group conversations, WhatsApp has been shown to enable the formation of a rich underlying network, crossing the boundaries of existing groups, and with structural properties that favor information dissemination at large. Indeed, WhatsApp has reportedly been used as a forum of misinformation campaigns with significant social, political and economic consequences in several countries.In this article, we aim at complementing recent studies on misinformation spread on WhatsApp, mostly focused on content properties and propagation dynamics, by looking into the network that connects users sharing the same piece of content. Specifically, we present a hierarchical network-oriented characterization of the users engaged in misinformation spread by focusing on three perspectives: individuals, WhatsApp groups and user communities, i.e., groupings of users who, intentionally or not, share the same content disproportionately often. By analyzing sharing and network topological properties, our study offers valuable insights into how WhatsApp users leverage the underlying network connecting different groups to gain large reach in the spread of misinformation in the platform. 相似文献
10.
基于主题细分的社交网络用户间交互特征分析 总被引：1，自引：0，他引：1
[目的/意义]针对一微博子网,从主题细分的角度对用户间历史交互记录进行研究,发现用户间交互的主题偏好特征,以期从微观层面了解用户信息传播行为的规律。[方法/过程]通过用户实例分析得出对用户间交互进行主题细分的必要性;利用主题模型(LDA)对用户间历史交互记录进行主题细分,采用多维向量表示用户间在不同主题下的交互强度;通过统计分析和网络分析方法探索用户间交互的主题特征。[结果/结论]各主题下用户间交互强度的分布具有长尾特征;用户间的交互内容在时序上具有主题相关性;基于多维的用户间交互强度,可抽取出特定主题下的用户交互子网。用户间交互在时序上具有主题相关性这一特征,以及特定主题的用户交互子网,可用于对特定主题的信息传播进行监控和预测。 相似文献
11.
随着开源软件(OSS)的普及，作为其承载平台的开源软件社区，也得到了用户的广泛使用。但用户往往仅从社区获取开源软件或代码，而缺乏知识贡献的意愿，这将影响OSS社区的持续运营和发展。基于社会影响理论，本文建立了开源软件社区用户知识贡献行为模型，考察了顺从、认同、内化三种社会影响机制对用户行为的作用。研究收集了351份有效数据，采用结构方程模型(SEM)进行分析。研究结果显示，社会认可（认同机制）是影响开源软件社区用户知识贡献意愿的主要因素，群体规范（内化机制）对用户的贡献意愿没有显著作用，而主观规范（顺从机制）阻碍用户的贡献意愿形成。研究结果启示开源软件社区需重视建立用户的社会认同，从而促进其知识贡献行为，确保社区的成功。 相似文献
12.
[目的/意义]分析通证知识社区的知识分享网络结构,有助于把握区块链背景下的虚拟社区知识分享和传播规律。[方法/过程]以国内通证知识社区代表——币乎网为研究对象,通过网络爬虫方式获取币乎用户样本数据,采用社会网络分析和内容分析方法,运用UCINET工具对社区用户的知识分享网络进行网络特征分析。[结果/结论]样本网整体呈现出小世界效应和无标度网络特征;中心性高的核心用户对社区知识贡献和传播的影响力较高;通证激励有助于挖掘社区中的优质内容。 相似文献
13.
Chunlin Li Jingpan Bai Zhao Wenjun Yang Xihao 《Information processing & management》2019,56(1):91-109
Recently, social network has been paid more and more attention by people. Inaccurate community detection in social network can provide better product designs, accurate information recommendation and public services. Thus, the community detection (CD) algorithm based on network topology and user interests is proposed in this paper. This paper mainly includes two parts. In first part, the focused crawler algorithm is used to acquire the personal tags from the tags posted by other users. Then, the tags are selected from the tag set based on the TFIDF weighting scheme, the semantic extension of tags and the user semantic model. In addition, the tag vector of user interests is derived with the respective tag weight calculated by the improved PageRank algorithm. In second part, for detecting communities, an initial social network, which consists of the direct and unweighted edges and the vertexes with interest vectors, is constructed by considering the following/follower relationship. Furthermore, initial social network is converted into a new social network including the undirected and weighted edges. Then, the weights are calculated by the direction and the interest vectors in the initial social network and the similarity between edges is calculated by the edge weights. The communities are detected by the hierarchical clustering algorithm based on the edge-weighted similarity. Finally, the number of detected communities is detected by the partition density. Also, the extensively experimental study shows that the performance of the proposed user interest detection (PUID) algorithm is better than that of CF algorithm and TFIDF algorithm with respect to F-measure, Precision and Recall. Moreover, Precision of the proposed community detection (PCD) algorithm is improved, on average, up to 8.21% comparing with that of Newman algorithm and up to 41.17% comparing with that of CPM algorithm. 相似文献
14.
Veera Kangaspunta 《The Information Society》2013,29(5):275-288
AbstractThe research on online news comments has been dominated by a normative approach and has centered on media engagement. Normativity and media dominance have also featured big in the theoretical discussions on the public sphere. This article presents a case study of online news comments, combining a novel methodological testing of social network hypotheses to examine user–user interactions in online comments with a conceptual discussion of the potential connections between social network research and theories of the public. The social network analysis in this study indicated that users (online commentators) do not constitute highly dense networks, although their relations can be studied as social networks. However, this analysis can only explore limited features of this online phenomenon and requires complementary methods. From a conceptual perspective, this article confirms the role of shared issue for a potential public and also emphasizes the importance of context, actors, and meanings for understanding the public. 相似文献
15.
基于知识质量的社会化问答社区用户知识共享的演化博弈分析 总被引：1，自引：0，他引：1
[目的/意义]社会化问答社区作为网络知识交互平台,其持续发展的关键在于促进用户知识共享,提升共享知识质量。[方法/过程]通过社区知识"质"与"量"的细分,将社区用户主动或被动参与社区知识共享获得的知识收益区分为"质"的收益与"量"的收益,并构建社会化问答社区用户知识共享的演化博弈模型,探讨不同博弈假设下问答社区知识共享的均衡状态。[结果/结论]通过仿真显示,社会化问答社区共享知识质量与用户共享行为策略会受到用户共享意愿与能力、用户认可与社区激励、感知共享成本等因素的影响。 相似文献
16.
Seyed Amin Mirlohi Falavarjani Fattane Zarrinkalam Jelena Jovanovic Ebrahim Bagheri Ali A. Ghorbani 《Information processing & management》2019,56(6):102070
The ever increasing presence of online social networks in users’ daily lives has led to the interplay between users’ online and offline activities. There have already been several works that have studied the impact of users’ online activities on their offline behavior, e.g., the impact of interaction with friends on an exercise social network on the number of daily steps. In this paper, we consider the inverse to what has already been studied and report on our extensive study that explores the potential causal effects of users’ offline activities on their online social behavior. The objective of our work is to understand whether the activities that users are involved with in their real daily life, which place them within or away from social situations, have any direct causal impact on their behavior in online social networks. Our work is motivated by the theory of normative social influence, which argues that individuals may show behaviors or express opinions that conform to those of the community for the sake of being accepted or from fear of rejection or isolation. We have collected data from two online social networks, namely Twitter and Foursquare, and systematically aligned user content on both social networks. On this basis, we have performed a natural experiment that took the form of an interrupted time series with a comparison group design to study whether users’ socially situated offline activities exhibited through their Foursquare check-ins impact their online behavior captured through the content they share on Twitter. Our main findings can be summarised as follows (1) a change in users’ offline behavior that affects the level of users’ exposure to social situations, e.g., starting to go to the gym or discontinuing frequenting bars, can have a causal impact on users’ online topical interests and sentiment; and (2) the causal relations between users’ socially situated offline activities and their online social behavior can be used to build effective predictive models of users’ online topical interests and sentiments. 相似文献
17.
《Information processing & management》2023,60(5):103464
Existing approaches to learning path recommendation for online learning communities mainly rely on the individual characteristics of users or the historical records of their learning processes, but pay less attention to the semantics of users’ postings and the context. To facilitate the knowledge understanding and personalized learning of users in online learning communities, it is necessary to conduct a fine-grained analysis of user data to capture their dynamical learning characteristics and potential knowledge levels, so as to recommend appropriate learning paths. In this paper, we propose a fine-grained and multi-context-aware learning path recommendation model for online learning communities based on a knowledge graph. First, we design a multidimensional knowledge graph to solve the problem of monotonous and incomplete entity information presentation of the single layer knowledge graph. Second, we use the topic preference features of users’ postings to determine the starting point of learning paths. We then strengthen the distant relationship of knowledge in the global context using the multidimensional knowledge graph when generating and recommending learning paths. Finally, we build a user background similarity matrix to establish user connections in the local context to recommend users with similar knowledge levels and learning preferences and synchronize their subsequent postings. Experiment results show that the proposed model can recommend appropriate learning paths for users, and the recommended similar users and postings are effective. 相似文献
18.
[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
19.
【目的/意义】本文利用用户在健康信息问答过程中产生的真实数据,从网络结构和信息主题两个维度对网
络健康社区中的健康信息传播网络及主题特征进行研究,进而为社区的建设和维护提供建议。【方法/过程】首先,
利用社会网络分析法对不同时间阶段的健康信息传播网络进行指标测度和可视化呈现,探究其网络结构特征;然
后结合LDA和已有词表,对社区内健康信息进行主题识别和提取,分析健康信息主题的分布及其变化趋势。【结果/
结论】本研究发现实验选取的网络健康社区内部的健康信息传播网络具有小世界效应,用户流动性大,关键节点变
更快;用户健康信息问答的内容集中在若干主题,且部分主题随时间呈现一定变化趋势。【创新/局限】网络健康信
息的生成和传播源于用户之间的信息交互。本文从网络结构和信息主题两个维度开展研究,更符合其内部机理,
研究更加深入和具象;网络健康社区种类众多,本文只选择其中一个社区进行研究,可能存在一定的局限性。 相似文献
20.
虚拟社区用户忠诚对于虚拟社区的长期可持续性发展尤为重要。本研究以知识分享行为（知识共享数量和质量）为中介变量,探讨社会资本（信任、社会联结性、共同愿景）和外部动机（互惠动机、声誉动机）对社区忠诚的作用机理,并通过结构方程模型进行验证。研究结果表明,信任和外部动机通过知识共享数量和质量对社区忠诚有显著正向影响,社会联结性仅通过知识共享数量对社区忠诚有显著影响;信任和外部动机对知识共享数量和质量有显著正向影响,社会联结性仅显著影响知识共享数量,而共同愿景对知识共享数量和质量均无显著影响;知识共享行为对社区忠诚有显著正向影响,并且知识共享质量对社区忠诚的影响更显著。该结论为企业重视虚拟社区用户知识分享行为从而吸引和留住虚拟社区用户提供了决策支持,为企业更好的运营虚拟社区并利用虚拟社区获得可持续发展提供了重要启示。 相似文献