共查询到20条相似文献，搜索用时 15 毫秒
1.
Question answering websites are becoming an ever more popular knowledge sharing platform. On such websites, people may ask any type of question and then wait for someone else to answer the question. However, in this manner, askers may not obtain correct answers from appropriate experts. Recently, various approaches have been proposed to automatically find experts in question answering websites. In this paper, we propose a novel hybrid approach to effectively find experts for the category of the target question in question answering websites. Our approach considers user subject relevance, user reputation and authority of a category in finding experts. A user’s subject relevance denotes the relevance of a user’s domain knowledge to the target question. A user’s reputation is derived from the user’s historical question-answering records, while user authority is derived from link analysis. Moreover, our proposed approach has been extended to develop a question dependent approach that considers the relevance of historical questions to the target question in deriving user domain knowledge, reputation and authority. We used a dataset obtained from Yahoo! Answer Taiwan to evaluate our approach. Our experiment results show that our proposed methods outperform other conventional methods. 相似文献
2.
Michael J. Cole Jacek Gwizdka Chang Liu Nicholas J. Belkin Xiangmin Zhang 《Information processing & management》2013
The acquisition of information and the search interaction process is influenced strongly by a person’s use of their knowledge of the domain and the task. In this paper we show that a user’s level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user’s information acquisition process during search using only measurements of eye movement patterns. In a user study (n = 40) of search in the domain of genomics, a representation of the participant’s domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n = 409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual’s level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user’s level of knowledge based on real-time measurements of eye movement patterns during a task session. 相似文献
3.
Information filtering (IF) systems usually filter data items by correlating a set of terms representing the user’s interest (a user profile) with similar sets of terms representing the data items. Many techniques can be employed for constructing user profiles automatically, but they usually yield large sets of term. Various dimensionality-reduction techniques can be applied in order to reduce the number of terms in a user profile. We describe a new terms selection technique including a dimensionality-reduction mechanism which is based on the analysis of a trained artificial neural network (ANN) model. Its novel feature is the identification of an optimal set of terms that can classify correctly data items that are relevant to a user. The proposed technique was compared with the classical Rocchio algorithm. We found that when using all the distinct terms in the training set to train an ANN, the Rocchio algorithm outperforms the ANN based filtering system, but after applying the new dimensionality-reduction technique, leaving only an optimal set of terms, the improved ANN technique outperformed both the original ANN and the Rocchio algorithm. 相似文献
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Wanpeng Song Liu Wenyin Naijie Gu Xiaojun Quan Tianyong Hao 《Information processing & management》2011
Question categorization, which suggests one of a set of predefined categories to a user’s question according to the question’s topic or content, is a useful technique in user-interactive question answering systems. In this paper, we propose an automatic method for question categorization in a user-interactive question answering system. This method includes four steps: feature space construction, topic-wise words identification and weighting, semantic mapping, and similarity calculation. We firstly construct the feature space based on all accumulated questions and calculate the feature vector of each predefined category which contains certain accumulated questions. When a new question is posted, the semantic pattern of the question is used to identify and weigh the important words of the question. After that, the question is semantically mapped into the constructed feature space to enrich its representation. Finally, the similarity between the question and each category is calculated based on their feature vectors. The category with the highest similarity is assigned to the question. The experimental results show that our proposed method achieves good categorization precision and outperforms the traditional categorization methods on the selected test questions. 相似文献
6.
Content-based filtering can be deployed for personalised information dissemination on the web, but this is a possibility that has been largely ignored. Nowadays, there are no successful content-based filtering applications available online. Nootropia is an immune-inspired user profiling model for content-based filtering. It has the advantageous property to be able to represent a user’s multiple interests and adapt to a variety of changes in them. In this paper we describe our early efforts to develop real world personalisation services based on Nootropia. We present, the architecture, implementation, usage and evaluation of the personalised news and paper aggregator, which aggregates news and papers that are relevant to an individual’s interests. Our user study shows that Nootropia can effectively learn a user’s interests and identify relevant information. It also indicates that information filtering is a complicated task with many factors affecting its successful application in a real situation. 相似文献
7.
A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individual’s capability to survey. Music recommendation is considered a popular application area. In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focus on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to utilize information extracted directly from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. In addition, this model has been extended for improved recommendation performance by utilizing audio features that help alleviate three well-known problems associated with data sparseness in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experimental results based on two real-world data sets lead us to believe that content information is crucial in achieving better personalized recommendation beyond user ratings. We further show how primitive audio features can be combined into aggregate features for the proposed CRMS and analyze their influences on recommendation performance. Although this model was developed originally for music collaborative recommendation based on audio features, our experiment with the movie data set demonstrates that it can be applied to other domains. 相似文献
8.
《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
9.
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
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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
12.
Recently, question series have become one focus of research in question answering. These series are comprised of individual factoid, list, and “other” questions organized around a central topic, and represent abstractions of user–system dialogs. Existing evaluation methodologies have yet to catch up with this richer task model, as they fail to take into account contextual dependencies and different user behaviors. This paper presents a novel simulation-based methodology for evaluating answers to question series that addresses some of these shortcomings. Using this methodology, we examine two different behavior models: a “QA-styled” user and an “IR-styled” user. Results suggest that an off-the-shelf document retrieval system is competitive with state-of-the-art QA systems in this task. Advantages and limitations of evaluations based on user simulations are also discussed. 相似文献
13.
《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
14.
Web sites often provide the first impression of an organization. For many organizations, web sites are crucial to ensure sales or to procure services within. When a person opens a web site, the first impression is probably made in a few seconds, and the user will either stay or move on to the next site on the basis of many factors. One of the factors that may influence users to stay or go is the page aesthetics. Another reason may involve a user’s judgment about the site’s credibility. This study explores the possible link between page aesthetics and a user’s judgment of the site’s credibility. Our findings indicate that when the same content is presented using different levels of aesthetic treatment, the content with a higher aesthetic treatment was judged as having higher credibility. We call this the amelioration effect of visual design and aesthetics on content credibility. Our study suggests that this effect is operational within the first few seconds in which a user views a web page. Given the same content, a higher aesthetic treatment will increase perceived credibility. 相似文献
15.
Hyun-Yong Noh Author VitaeJin-Hyung Lee Author Vitae Sae-Won Oh Author VitaeKeum-Sung Hwang Author Vitae Sung-Bae ChoAuthor Vitae 《Information processing & management》2012,48(1):1-12
Personal mobile devices such as cellular phones, smart phones and PMPs have advanced incredibly in the past decade. The mobile technologies make research on the life log and user-context awareness feasible. In other words, sensors in mobile devices can collect the variety of user’s information, and various works have been conducted using that information. Most of works used a user’s location information as the most useful clue to recognize the user context. However, the location information in the conventional works usually depends on a GPS receiver that has limited function, because it cannot localize a person in a building and thus lowers the performance of the user-context awareness. This paper develops a system to solve such problems and to infer a user’s hidden information more accurately using Bayesian network and indoor-location information. Also, this paper presents a new technique for localization in a building using a decision tree and signals for the Wireless LAN because the decision tree has many advantages which outweigh other localization techniques. 相似文献
16.
Over time, researchers have acknowledged the importance of understanding the users’ strategies in the design of search systems. However, when involving users in the comparison of search systems, methodological challenges still exist as researchers are pondering on how to handle the variability that human participants bring to the comparisons. This paper present methods for controlling the complexity of user-centered evaluations of search user interfaces through within-subjects designs, balanced task sets, time limitations, pre-formulated queries, cached result pages, and through limiting the users’ access to result documents. Additionally, we will present our experiences in using three measures – search speed, qualified search speed, and immediate accuracy – to facilitate the comparison of different search systems over studies. 相似文献
17.
A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques 总被引：1，自引：0，他引：1
A key driver for next generation web information retrieval systems is becoming the degree to which a user’s search and presentation experience is adapted to individual user properties and contexts of use. Over the past decades, two parallel threads of personalisation research have emerged, one originating in the document space in the area of Personalised Information Retrieval (PIR) and the other arising from the hypertext space in the field of Adaptive Hypermedia (AH). 相似文献
18.
This article analyzes the impact of a computer simulation (business game) on the users’ perceived learning. The theoretical model developed in this paper is derived from collaborative learning and human–computer interaction assumptions. The hypotheses relating to group dynamics and the user–computer interface design with the users’ perceived learning are tested using the business game “FirmReality” on a sample of 173 respondents. Multiple regression and qualitative results show that, in a computer-based simulation context, perceived learning is influenced more by human–computer interaction factors than by group dynamics. Furthermore, results point out a negative effect of interaction with the instructor on individual perceived learning. These results support the possibility to automate the interaction between teacher and students. 相似文献
19.
Analyzing review sentiments and product images by parallel deep nets for personalized recommendation
《Information processing & management》2023,60(1):103166
Recommendation is an effective marketing tool widely used in the e-commerce business, and can be made based on ratings predicted from the rating data of purchased items. To improve the accuracy of rating prediction, user reviews or product images have been used separately as side information to learn the latent features of users (items). In this study, we developed a hybrid approach to analyze both user sentiments from review texts and user preferences from item images to make item recommendations more personalized for users. The hybrid model consists of two parallel modules to perform a procedure named the multiscale semantic and visual analyses (MSVA). The first module is designated to conduct semantic analysis on review documents in various aspects with word-aware and scale-aware attention mechanisms, while the second module is assigned to extract visual features with block-aware and visual-aware attention mechanisms. The MSVA model was trained, validated and tested using Amazon Product Data containing sampled reviews varying from 492,970 to 1 million records across 22 different domains. Three state-of-the-art recommendation models were used as the baselines for performance comparisons. Averagely, MSVA reduced the mean squared error (MSE) of predicted ratings by 6.00%, 3.14% and 3.25% as opposed to the three baselines. It was demonstrated that combining semantic and visual analyses enhanced MSVA's performance across a wide variety of products, and the multiscale scheme used in both the review and visual modules of MSVA made significant contributions to the rating prediction. 相似文献
20.
《Information processing & management》2020,57(5):102270
Identifying perceived emotional content of music constitutes an important aspect of easy and efficient search, retrieval, and management of the media. One of the most promising use cases of music organization is an emotion-based playlist, where automatic music emotion recognition plays a significant role in providing emotion related information, which is otherwise, generally unavailable. Based on the importance of the auditory system in emotional recognition and processing, in this study, we propose a new cochleogram-based system for detecting the affective musical content. To effectively simulate the response of the human auditory periphery, the music audio signal is processed by a detailed biophysical cochlear model, thus obtaining an output that closely matches the characteristics of human hearing. In this proposed approach, based on the cochleogram images, which we construct directly from the response of the basilar membrane, a convolutional neural network (CNN) is used to extract the relevant music features. To validate the practical implications of the proposed approach with regard to its possible integration in different digital music libraries, an extensive study was conducted to evaluate the predictive performance of our approach in different aspects of music emotion recognition. The proposed approach was evaluated on publicly available 1000 songs database and the experimental results showed that it performed better in comparison with common musical features (such as tempo, mode, pitch, clarity, and perceptually motivated mel-frequency cepstral coefficients (MFCC)) as well as official ”MediaEval” challenge results on the same reference database. Our findings clearly show that the proposed approach can lead to better music emotion recognition performance and be used as part of a state-of-the-art music information retrieval system. 相似文献