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【目的/意义】“信息过载”问题不利于用户快速准确获取目标信息,本文提出构建社交媒体基因图谱并应
用于信息推荐领域,为用户信息推荐提供新思路。【目的/意义】本文将社交媒体分解为社交媒体用户和社交信息内
容两大主体部分,借助生物学基因图谱概念,建立两大主体基因群并分别进行基因提取,构建社交媒体基因图谱,
并通过社交媒体基因图谱唯一辨识用户实体偏好进行信息推荐。【结果/结论】构建基于社交媒体基因图谱的信息
推荐模型,考虑用户内部及信息环境对基因图谱的影响,进行模型优化,为用户提供更加精准的信息推荐内容。【创
新/局限】基因的变异无法准确预估和控制,需要进一步研究引起基因变异的因素及变异方向,通过基因的可复制
性更加高效准确地对个人用户社交媒体基因图谱进行构建,进而实现其在信息推荐领域的应用。 相似文献
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《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
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With the expansion of information on the web, recommendation systems have become one of the most powerful resources to ease the task of users. Traditional recommendation systems (RS) suggest items based only on feedback submitted by users in form of ratings. These RS are not competent to deal with definite user preferences due to emerging and situation dependent user-generated content on social media, these situations are known as contextual dimensions. Though the relationship between contextual dimensions and user’s preferences has been demonstrated in various studies, only a few studies have explored about prioritization of varying contextual dimensions. The usage of all contextual dimensions unnecessary raises the computational complexity and negatively influences the recommendation results. Thus, the initial impetus has been made to construct a neural network in order to determine the pertinent contextual dimensions. The experiments are conducted on real-world movies data-LDOS CoMoDa dataset. The results of neural networks demonstrate that contextual dimensions have a significant effect on users’ preferences which in turn exerts an intense impact on the satisfaction level of users. Finally, tensor factorization model is employed to evaluate and validate accuracy by including neural network’s identified pertinent dimensions which are modeled as tensors. The result shows improvement in recommendation accuracy by a wider margin due to the inclusion of the pertinent dimensions in comparison to irrelevant dimensions. The theoretical and managerial implications are discussed. 相似文献
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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
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Socially similar social media users can be defined as users whose frequently visited locations in their social media histories are similar. Discovering socially similar social media users is important for several applications, such as, community detection, friendship analysis, location recommendation, urban planning, and anomaly user and behavior detection. Discovering socially similar users is challenging due to dataset size and dimensions, spam behaviors of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar social media users out of social media datasets using spatial and temporal information. However, most of these studies rely on trajectory pattern mining methods or take into account semantic information of social media datasets. Limited number of studies focus on discovering similar users based on their social media location histories. In this study, to discover socially similar users, frequently visited or socially important locations of social media users are taken into account instead of all locations that users visited. A new interest measure, which is based on Levenshtein distance, was proposed to quantify user similarity based on their socially important locations and two algorithms were developed using the proposed method and interest measure. The algorithms were experimentally evaluated on a real-life Twitter dataset. The results show that the proposed algorithms could successfully discover similar social media users based on their socially important locations. 相似文献
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【目的/意义】构建基于用户动态画像的学术新媒体信息精准推荐模型是满足学术新媒体用户对学术信息
资源精准化、个性化与专业化的要求,同时也是提高学术信息流转效率以及价值增值的有效途径。【方法/过程】在
探究学术新媒体信息流转模型的基础上,进一步分析学术新媒体用户需求与分层画像,重构学术新媒体用户画像
步骤,构建基于用户动态画像的学术新媒体信息精准推荐模型。【结果/结论】基于用户动态画像的学术新媒体信息
精准推荐模型能够实现学术信息资源与用户的精准对接,提升用户忠诚度,更好地服务科研工作者的学术活动。
【创新/局限】从理论框架角度分析与构建学术新媒体信息推荐模型,后续将重点研究模型的技术实现与实践应用。 相似文献
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借鉴"认知—情感—意动"理论框架,从社区信息与网站特性、个体和群体3个方面探索在线用户社区成员认知因素对其社区持续参与意愿的影响。通过国内智能手机产品在线用户社区的问卷调查收集329份有效问卷,应用PLS结构方程模型进行模型验证。结果发现,社区信息与网站特性方面的感知信息质量、感知网站互动性以及个体层面的感知自我效能、感知社会提升对社区成员的社会存在感有显著促进作用;群体层面的感知相似性和感知信任对社会存在感和社区认同感都有促进作用。上述三方面因素又以社会存在感和社区认同感这两种情感因素为中介对社区成员的持续参与意愿产生积极影响。 相似文献
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Jun Wang Maarten Clements Jie Yang Arjen P. de Vries Marcel J.T. Reinders 《Information processing & management》2010,46(1):58-70
Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata.This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user’s tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user’s own preference and the opinion of others. 相似文献
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《Information processing & management》2020,57(6):102337
A large volume of data flowing throughout location-based social networks (LBSN) gives support to the recommendation of points-of-interest (POI). One of the major challenges that significantly affects the precision of recommendation is to find dynamic spatio-temporal patterns of visiting behaviors, which can hardly be figured out because of the multiple side factors. To confront this difficulty, we jointly study the effects of users’ social relationships, textual reviews, and POIs’ geographical proximity in order to excavate complex spatio-temporal patterns of visiting behaviors when the data quality is unreliable for location recommendation in spatio-temporal social networks. We craft a novel framework that recommends any user the POIs with effectiveness. The framework contains two significant techniques: (i) a network embedding method is adopted to learn the vectors of users and POIs in an embedding space of low dimension; (ii) a dynamic factor graph model is proposed to model various factors such as the correlation of vectors in the previous phase. A collection of experiments was carried out on two real large-scale datasets, and the experimental outcomes demonstrate the supremacy of the proposed method over the most advanced baseline algorithms owing to its highly effective and efficient performance of POI recommendation. 相似文献
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《Information processing & management》2023,60(3):103256
Music has a close relationship with people's emotion and mental status. Music recommendation has both economic and social benefits. Unfortunately, most existing music recommendation methods were constructed based on genre features (e.g., style and album), which cannot meet the emotional needs of listeners. Furthermore, the “filter bubble” effect may make the situation even worse, when a user seeks music for emotional support. In this study, we designed a novel emotion-based personalized music recommendation framework to meet users’ emotional needs and help improve their mental status. In our framework, we designed a LSTM-based model to select the most suitable music based on users’ mood in previous period and current emotion stimulus. A care factor was used to adjust the results so that users’ mental status could be improved by the recommendation. The empirical experiments and user study showed that the recommendations of our novel framework are precise and helpful for users. 相似文献
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《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
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The widespread use of the Internet and the constant increase in users of social media platforms has made a large amount of textual data available. This represents a valuable source of information about the changes in people’s opinions and feelings. This paper presents the application of Emotional Text Mining (ETM) in the field of brand management. ETM is an unsupervised procedure aiming to profile social media users. It is based on a bottom-up approach to classify unstructured data for the identification of social media users’ representations and sentiments about a topic. It is a fast and simple procedure to extract meaningful information from a large collection of texts. As customer profiling is relevant for brand management, we illustrate a business application of ETM on Twitter messages concerning a well-known sportswear brand in order to show the potential of this procedure, highlighting the characteristics of Twitter user communities in terms of product preferences, representations, and sentiments. 相似文献
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《Information processing & management》2022,59(1):102755
General recommenders and sequential recommenders are two modeling paradigms of recommender. The main focus of a general recommender is to identify long-term user preferences, while the user’s sequential behaviors are ignored and sequential recommenders try to capture short-term user preferences by exploring item-to-item relations, failing to consider general user preferences. Recently, better performance improvement is reported by combining these two types of recommenders. However, most of the previous works typically treat each item separately and assume that each user–item interaction in a sequence is independent. This may be a too simplistic assumption, since there may be a particular purpose behind buying the successive item in a sequence. In fact, a user makes a decision through two sequential processes, i.e., start shopping with a particular intention and then select a specific item which satisfies her/his preferences under this intention. Moreover, different users usually have different purposes and preferences, and the same user may have various intentions. Thus, different users may click on the same items with an attention on a different purpose. Therefore, a user’s behavior pattern is not completely exploited in most of the current methods and they neglect the distinction between users’ purposes and their preferences. To alleviate those problems, we propose a novel method named, CAN, which takes both users’ purposes and preferences into account for the next-item recommendation. We propose to use Purpose-Specific Attention Unit (PSAU) in order to discriminately learn the representations of user purpose and preference. The experimental results on real-world datasets demonstrate the advantages of our approach over the state-of-the-art methods. 相似文献
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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
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《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
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《Information processing & management》2020,57(4):102251
The primary aim of location recommendation is to predict users’ future movement by modeling user preference. Multiple types of information have been adopted in profiling users; however, simultaneously combining them for a better recommendation is challenging. In this study, a novel location recommendation method that incorporates geographical, categorical, and social preferences with location popularity is proposed. Experimental results on two public datasets show that the proposed method significantly outperforms two state-of-the-art recommendation methods. Geographical preference generally shows more importance than both categorical and social preferences. A category hierarchy that unleashes the independent assumption of location tags improves categorical preference. Location popularity proves to be a useful metric in ranking candidate locations. The findings of this study can provide practical guidelines for location recommendation services. 相似文献
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社会化媒体的兴起推动了用户参与创新的方式由线下转向线上,使用户参与创新日益向分布式、网络式和协作式转变.学术界对此变化的关注逐渐升温,并产生了一些创造性的成果.本文基于该主题的国内外研究进展,首先解释了社会化媒体情景下用户参与创新的起源和内涵,并对实践中出现的多种基于社会化媒体的用户创新方式进行了类型划分.进一步地,从社会化媒体情景下用户参与创新的前因、过程和效果3个方面总结了当前研究的主要成果.在此基础上,构建了社会化媒体情景下用户参与创新的系统性解释框架.最后,就社会化媒体情境下用户参与服务创新、识别有效创新用户、用户知识转移过程以及创新生态网络等问题对未来研究进行了展望. 相似文献