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1.
Community question answering (CQA) services that enable users to ask and answer questions have become popular on the internet. However, lots of new questions usually cannot be resolved by appropriate answerers effectively. To address this question routing task, in this paper, we treat it as a ranking problem and rank the potential answerers by the probability that they are able to solve the given new question. We utilize tensor model and topic model simultaneously to extract latent semantic relations among asker, question and answerer. Then, we propose a learning procedure based on the above models to get optimal ranking of answerers for new questions by optimizing the multi-class AUC (Area Under the ROC Curve). Experimental results on two real-world CQA datasets show that the proposed method is able to predict appropriate answerers for new questions and outperforms other state-of-the-art approaches. 相似文献
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《Information processing & management》2019,56(3):939-954
Recently, the high popularity of social networks accelerates the development of item recommendation. Integrating the influence diffusion of social networks in recommendation systems is a challenging task since topic distribution over users and items is latent and user topic interest may change over time. In this paper, we propose a dynamic generative model for item recommendation which captures the potential influence logs based on the community-level topic influence diffusion to infer the latent topic distribution over users and items. Our model enables tracking the time-varying distributions of topic interest and topic popularity over communities in social networks. A collapsed Gibbs sampling algorithm is proposed to train the model, and an improved diversification algorithm is proposed to obtain item diversified recommendation list. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method. The results validate our approach and show the superiority of our method compared with state-of-the-art diversified recommendation methods. 相似文献
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文章结合眼动追踪技术最新的发展,全面分析研究了该技术在数字图书馆中的具体应用,包括数字图书馆用户分析、数字图书馆推荐系统设计、数字图书馆服务提升和移动用户数字图书馆服务功能拓展,为搭建高效合理的数字图书馆提供了一种新思路,进而提升数字图书馆的实用性。 相似文献
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《Information processing & management》2022,59(4):102982
Recommender system as an effective method to reduce information overload has been widely used in the e-commerce field. Existing studies mainly capture semantic features by considering user-item interactions or behavioral history records, which ignores the sparsity of interactions and the drift of user preferences. To cope with these challenges, we introduce the recently popular Graph Neural Networks (GNN) and propose an Interest Evolution-driven Gated Neighborhood (IEGN) aggregation representation model which can capture accurate user representation and track the evolution of user interests. Specifically, in IEGN, we explicitly model the relational information between neighbor nodes by introducing the gated adaptive propagation mechanism. Then, a personalized time interval function is designed to track the evolution of user interests. In addition, a high-order convolutional pooling operation is used to capture the correlation among the short-term interaction sequence. The user preferences are predicted by the fusion of user dynamic preferences and short-term interaction features. Extensive experiments on Amazon and Alibaba datasets show that IEGN outperforms several state-of-the-art methods in recommendation tasks. 相似文献
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In this paper, we propose a generative model, the Topic-based User Interest (TUI) model, to capture the user interest in the User-Interactive Question Answering (UIQA) systems. Specifically, our method aims to model the user interest in the UIQA systems with latent topic method, and extract interests for users by mining the questions they asked, the categories they participated in and relevant answer providers. We apply the TUI model to the application of question recommendation, which automatically recommends to certain user appropriate questions he might be interested in. Data collection from Yahoo! Answers is used to evaluate the performance of the proposed model in question recommendation, and the experimental results show the effectiveness of our proposed model. 相似文献
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《International Journal of Information Management》2016,36(3):397-402
Online video recommender systems help users find videos suitable for their preferences. However, they have difficulty in identifying dynamic user preferences. In this study, we propose a new recommendation procedure using changes of users’ facial expressions captured every moment. Facial expressions portray the users’ actual emotions about videos. We can utilize them to discover dynamic user preferences. Further, because the proposed procedure does not rely on historical rating or purchase records, it properly addresses the new user problem, that is, the difficulty in recommending products to users whose past rating or purchase records are not available. To validate the recommendation procedure, we conducted experiments with footwear commercial videos. Experiment results show that the proposed procedure outperforms benchmark systems including a random recommendation, an average rating approach, and a typical collaborative filtering approach for recommendation to both new and existing users. From the results, we conclude that facial expressions are a viable element in recommendation. 相似文献
7.
《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
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《Information processing & management》2020,57(3):102223
A significant problem common for different domains of applications is an issue of obtaining and keeping an influential position in the respective social media society. In this paper a new approach is proposed based on the analysis of roles of users in groups identified within society. Three different dimensions of user behavior are considered as key elements of these roles: Their activity, influence and cooperativeness/competition. Taking into account measures describing these dimensions, a set of roles characterizing behaviors of users in groups is formulated. We propose an original set of roles with their justification in sociological models, develop an easy extendable model of a social system and conduct experiments to allow us to define patterns describing stability and variability of given roles as well as statistics of transitions in time between these considered roles. To define the roles, we took into account different features of user interactions, both quantitative and qualitative. We propose an integrated approach to the analysis of role changes in the context of group evolution. We consider behavior of users in groups with different sizes and differences between them. We also analyse the stability of individual roles players by users in groups and often occurring transitions between individual roles. The obtained results, interpreted also from the sociological point of view, allow the formulation of general recommendations on which behaviors of users could ensure achieving and maintaining influential roles in social media. The most frequent patterns of transitions between roles are identified and significant similarities between them for two considered blog portals are described. The approaches and methods of analysis presented in the paper may be applied to support decisions leading to obtaining and maintaining influential positions in social media, which may be useful for the promotion of goods and services, leading business or political campaigns. 相似文献
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社会化推荐服务研究述评 总被引：2，自引：0，他引：2
Web2.0的广泛应用和用户需求的变革催生出了新的推荐服务理念--社会化推荐。以信息推荐研究演进为主线,学者沿着从内容推荐、协同推荐到混合推荐的技术路线,从用户模式推荐、信息组织推荐和关联关系推荐三个视角对信息推荐展开了研究;网络的社会化促进了研究的拓展,目前主要从社会化行为、关系网络和服务应用三个方面进行了社会化推荐研究,取得了一系列成果;但其推荐方式存在着固有的缺陷,应将研究重点转向基于用户关系的社会化推荐上。 相似文献
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如何准确分析用户行为,向用户提供满意的网页信息,一直以来都是个性化信息推荐系统设计的目标。本文在分析现有个性化信息推荐模型的基础上,针对以往研究在推荐兴趣时仅根据语义相关度进行协助性信息推荐,而忽略用户行为规律所包含的潜在兴趣信息的不足,尝试提出一个结合Web语义挖掘和FP-tree规则发现技术的个性化信息推荐模型。该模型利用本体对语义的明确化描述,在挖掘用户行为信息时获取用户兴趣偏好的语义信息,并利用FP-tree技术根据以获取的语义信息推理出用户兴趣行为模式,从而在信息推荐时不仅能准确理解用户兴趣偏好,也能根据用户潜在兴趣规律,推荐给用户更全面的网页信息。 相似文献
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Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that consists in inserting contextual and background information as new user–item pairs. The main advantage of this approach is that it can be applied in combination with several existing two-dimensional recommendation algorithms. To evaluate its effectiveness, we used the DaVI approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, and ran an extensive set of experiments in three different real world data sets. In addition, we have also compared our approach to the previously introduced combined reduction and weight post-filtering approaches. The empirical results strongly indicate that our approach enables the application of existing two-dimensional recommendation algorithms in multidimensional data, exploiting the useful information of these data to improve the predictive ability of top-N recommender systems. 相似文献
14.
随着电子商务的迅速发展，推荐系统与算法已经成为理论研究的热点。支持向量机是一种强大的分类工具，由其衍生出的支持向量机回归方法能很好地解决非线性回归问题。文中以电影推荐为例，引入支持向量机回归方法来分析项目的内容，构建用户模型，进而给出推荐。实验结果和理论分析表明这种推荐算法与传统协同过滤算法相比，能够明显提高推荐精度，并显著缩短了推荐所需时间；在大样本量情况下也能同样高效。 相似文献
15.
《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
16.
《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
17.
Users of Social Networking Sites (SNSs) like Facebook, LinkedIn or Twitter, are facing two problems: (1) it is difficult for them to keep track of their social friendships and friends’ social activities scattered across different SNSs; and (2) they are often overwhelmed by the huge amount of social data (friends’ updates and other activities). To address these two problems, we propose a user-centric system called “SocConnect” (Social Connect) for aggregating social data from different SNSs and allowing users to create personalized social and semantic contexts for their social data. Users can blend and group friends on different SNSs, and rate the friends and their activities as favourite, neutral or disliked. SocConnect then provides personalized recommendation of friends’ activities that may be interesting to each user, using machine learning techniques. A prototype is also implemented to demonstrate these functionalities of SocConnect. Evaluation on real users confirms that users generally like the proposed functionalities of our system, and machine learning can be effectively applied to provide personalized recommendation of friends’ activities and help users deal with cognitive overload. 相似文献
18.
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
19.
Victor Makarenkov Ido Guy Niva Hazon Tamar Meisels Bracha Shapira Lior Rokach 《Information processing & management》2019,56(5):1880-1893
In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative. It is hard to manually detect these implicit beliefs, intentions and moral foundations of the writers.We study and investigate two different tasks, each of which reflect the difficulty of detecting an implicit user’s knowledge, intent or belief that may be based on writer’s moral foundation: (1) political perspective detection in news articles (2) identification of informational vs. conversational questions in community question answering (CQA) archives. In both tasks we first describe new interesting annotated datasets and make the datasets publicly available. Second, we compare various classification algorithms, and show the differences in their performance on both tasks. Third, in political perspective detection task we utilize a narrative representation language of local press to identify perspective differences between presumably neutral American and British press. 相似文献
20.
Sesia J. Zhao Kem Z.K. Zhang Christian Wagner Huaping Chen 《International Journal of Information Management》2013
The recent prevalence of wiki applications has demonstrated that wikis have high potential in facilitating knowledge creation, sharing, integration, and utilization. As wikis are increasingly adopted in contexts like business, education, research, government, and the public, how to improve user contribution becomes an important concern for researchers and practitioners. In this research, we focus on the quality aspect of user contribution: contribution value. Building upon the critical mass theory and research on editing activities in wikis, this study investigates whether user interests and resources can increase contribution value for different types of users. We develop our research model and empirically test it using survey method and content analysis method in Wikipedia. The results demonstrate that (1) for users who emphasize substantive edits, depth of interests and depth of resources play more influential roles in affecting contribution value; and (2) for users who focus on non-substantive edits, breadth of interests and breadth of resources are more important in increasing contribution value. The findings suggest that contribution value develops in different patterns for two types of users. Implications for both theory and practice are discussed. 相似文献