Previous research on user payment behaviour in online community has predominantly focused on the pre-paid mode, with limited understanding of the antecedents influencing user payment behaviour in the post-paid mode. Drawing upon the dual-process theory and social identity theory, this study uses the text mining method based on the attention mechanism and the social network method to explore the influence of the quality of knowledge products and the credibility of contributors, focusing on the role of knowledge expertise and social identity. Using 247,013 replies to 32,373 questions from 3,631 seekers on Xueqiu.com (one of the most popular post-paid online Q&A communities in China), our empirical results found that informativeness, relevance of knowledge products, contributor honour, and experience significantly positively influence user payment behaviour, while timeliness of knowledge products exhibits a significant negative impact on user payment behaviour. Furthermore, contributor social interaction has a significant negative impact on user payment behaviour, contrary to the direction observed in the pre-paid mode. Moreover, the findings show that knowledge expertise and social identity have significant moderating effects in this process. This study provides assistance in developing diverse payment modes for knowledge platforms, and suggests insights for users to contribute knowledge more effectively.

It is becoming an important marketing strategy for enterprises to utilise user-generated content (UGC) to build brands and promote products on social media platforms, but little is known about the influence mechanism of the popularity of UGC related to brands and products. Drawing upon information adoption theory, this study developed a conceptual model to explain what makes UGC more popular in social media context. Using a dataset of 16,974 records related to the UGC about products from Xiaohongshu app, the most popular user experience sharing platform in China, the empirical study indicates that factors extracted from information quality and source credibility have significant effects on UGC popularity. Interestingly, text length and strength of text sentiment have different effects moderated by product types. In addition, media richness may lead to information redundancy and weaken the effect of text length. The findings offer an insightful understanding into the mechanism of utilising UGC in product marketing on social media platforms.

Cyber-physical systems (CPSs) connected in the form of the Industrial Internet of Things (IIoT) are susceptible to various security threats. Due to the extensive deployment of infrastructure for IIoT devices, the trustworthiness and security of data are among the major concerns in CPSs. Therefore, establishing security measures against potential threats through trust assessment and trust authentication has become a key goal. Blockchain has the characteristics of traceability, anonymity, transparency, etc., and can achieve trust authentication for trust assessment. In our work, we propose a lightweight decentralized authentication and trust management scheme for edge AI-enabled CPSs that supports access control on the basis of extended chaotic maps, which meets the privacy and security needs of data transmission in a broader sense. Moreover, we develop a trust model for checking the trustworthiness of data collected by smart devices/sensor nodes. A formal security analysis is executed by utilizing the broadly applicable real-or-random (RoR) model. Our scheme, which is different from previous methods, combines high security with relatively low communication and computational costs. Through an informal security analysis, we verify that our proposal is in compliance with the security requirements and can withstand various forms of attacks. Furthermore, the functionality and performance analysis results indicate that our method is better suited for lightweight validation in CPS networks while providing a higher level of security than other methods.

This study categorises mobile technology use into four types, taking into account two dimensions: the nature of the matters (work-related and non-work-related) and the boundaries (work and nonwork time). Furthermore, this study focuses on mobile technology use across boundaries. Drawing upon SDT, this study refines mobile technology use across work-nonwork boundaries into 2*2 categories based on the different ways in which it crosses the work-nonwork boundary, namely, work invading nonwork time (work-to-nonwork time), nonwork intruding on the realm of work time (nonwork-to-work time) as well as individual's willingness of behaviour (active and passive). Moreover, relying on COR theory, we introduce the mediating role of thriving at work (learning and vitality) in the relationship between across-boundary mobile technology use and job performance. This study shows that work-to-nonwork time active use and nonwork-to-work time active use have positive and inverted U-shaped effects on job performance separately, and both nonwork-to-work time and work-to-nonwork time passive use have negative effects on job performance. In addition, learning mediates the positive relationship between work-to-nonwork time active use and job performance, while vitality mediates the relationship between nonwork-to-work time active use and job performance, as well as between work-to-nonwork time passive use and job performance.

It is becoming more and more promising that marketers hire influencers to launch campaigns for spreading items (e.g., articles or videos about products) over social media platforms. Such social media influencer marketing may generate tremendous utility if the influencers persuade their followers to adopt the recommended items. This could further spur extensive spontaneous item propagation on social media. Although prior studies mainly focus on influencer-selection strategies by the influencers’ traits, marketers with a number of items are often requested to determine both influencers and marketing items. The appropriateness between influencers and items is critical, but rarely considered in prior influencer-identification methods. We thus formulate and solve a novel cost-effective social media influencer marketing problem to maximize marketers’ utility by selecting appropriate pairwise combinations of influencers and items (i.e., item-influencer pairs). In particular, we first model utility functions and propose a simulation-based method to estimate the appropriateness of arbitrarily given item-influencer pairs by their potential utility. With the estimated utility, we devise an algorithm to iteratively select appropriate item-influencer pairs under various realistic conditions, including marketers’ budget, influencers’ payments, item-user fitness, social propagation, and influencers’ marketing slots. We theoretically prove that the marketing utility achieved by our method is near-optimal. We also conduct extensive empirical experiments with three real-world data sets to verify the superiority of our method in terms of cost-effectiveness and computational efficiency. Lastly, we discuss insightful theoretical and practical implications. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This study was partially funded by the National Natural Science Foundation of China [Grants 72071125, 72031001, and 61972008]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.1246 .

Personalized points-of-interest (POI) recommendation is very important for improving the service quality of location-based social network applications. It has become one of the most popular research directions in the industry and academia. However, the realization of high-quality personalized POI recommendation faces three major challenges: (i) the interest drift issue caused by the spatiotemporal dynamics of user check-in behavior, (ii) how to integrate as much heterogeneous information as possible to alleviate data sparseness and cold start issues, and (iii) how to use implicit feedback to model complex high-order nonlinear user-POI interactions. To jointly address all these challenges, we propose the TGVx recommendation framework and establish a dynamic personalized POI deep recommendation model, where T and G respectively represent time and geographic factors, V represents out-of-town visitors, and x represents time slot number. TGVx is composed of x parallel TGV models where the TG module mines high-order nonlinear user-POI interaction relationships and integrates multisource heterogeneous information, and the V module transfers the check-in records of out-of-town visitors in hometowns and generates pseudo check-in records in the target city. Technically, we design a new unsupervised deep learning network T-SemiDAE for the TG module. We built a POI-word heterogeneous network for the V module and used graph embedding technology to match the most similar POIs across cities and transfer check-in records. The experimental results on the actual datasets show that the TGVx model is always better than other advanced models in terms of accuracy and diversity for local and out-of-town recommendation scenarios. Compared with the best baseline model semi-deep auto-encoder with a conditional layer the average improvement rates of accuracy and diversity of TGVx are 17.1% to 58.6% and 2.25% to 28.86%, respectively. In theory, our research effectively uses data science and analysis methods to design a recommender system. In practice, our research is motivated by practical problems, and the research results have high practical promotion value. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71831003, 72293563, and 72172025], the Dalian Science and Technology Talent Innovation Support Policy Project [The High-level Talent Innovation Team Project 2022RG17], and the Liaoning Provincial Department of Education [Basic Scientific Research Project LJKMZ20221582]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1286 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0010 ) at ( http://dx.doi.org/10.5281/zenodo.7407123 ).

Abstract Online health platforms play an important role in chronic disease management. Patients participate in online health platforms to receive and provide health‐related support from each other. However, there remains a debate about whether the influence of social interaction on patient health anxiety is linearly positive. Based on uncertainty, information overload, and the theory of motivational information management, we develop and test a model considering a potential curvilinear relationship between social interaction and health anxiety, as well as a moderating effect of health literacy. We collect patient interaction data from an online health platform based on chronic disease management in China and use text mining and econometrics to test our hypotheses. Specifically, we find an inverted U‐shaped relationship between informational provision and health anxiety. Our results also show that information receipt and emotion provision have U‐shaped relationships with health anxiety. Interestingly, health literacy can effectively alleviate the U‐shaped relationship between information receipt and health anxiety. These findings not only provide new insights into the literature on online patient interactions but also provide decision support for patients and platform managers.

Abstract Bullet screen comments (BSCs) are user‐generated short comments that appear as real‐time overlays on many video platforms, expressing the audience opinions and emotions about different aspects of the ongoing video. Unlike traditional long comments after a show, BSCs are often incomplete, ambiguous in context, and correlated over time. Current studies in sentiment analysis of BSCs rarely address these challenges, motivating us to develop an aspect‐level sentiment analysis framework. Our framework, BSCNET, is a pre‐trained language encoder‐based deep neural classifier designed to enhance semantic understanding. A novel neighbor context construction method is proposed to uncover latent contextual correlation among BSCs over time, and we also incorporate semi‐supervised learning to reduce labeling costs. The framework increases F1 (Macro) and accuracy by up to 10% and 10.2%, respectively. Additionally, we have developed two novel downstream tasks. The first is noisy BSCs identification, which reached F1 (Macro) and accuracy of 90.1% and 98.3%, respectively, through fine‐tuning the BSCNET. The second is the prediction of future episode popularity, where the MAPE is reduced by 11%–19.0% when incorporating sentiment features. Overall, this study provides a methodology reference for aspect‐level sentiment analysis of BSCs and highlights its potential for viewing experience or forthcoming content optimization.

The abundance of user ideas in the open innovation community poses a challenge of information overload for enterprises, making it difficult to effectively adopt ideas for product innovation. Existing studies primarily focus on evaluating the usefulness of user ideas, while overlooking the actual adoption outcomes of these ideas. Based on the information adoption model, this paper proposes a user idea adoption model, which integrates the two stages of idea collection and idea application. The authors creatively discuss the factors influencing the process from “perceived usefulness” to “adoption” of a user idea, and explore the complete mechanism of user idea adoption for product innovation. The results show that the information quality and source credibility of user ideas affect the perceived usefulness. The degree of user demand, innovation complexity, and competition intensity have moderating effects on the relationship between the perceived usefulness of user ideas and the results of user ideas adoption.

Abstract Establishing open innovation communities has evolved as an important product innovation and development strategy for companies. Yet, the success of such communities relies on the successful implementation of many user‐submitted ideas. Although extant literature has examined the impact of user experience and idea characteristics on idea implementation, little is known from the information input perspective, for example, feedback. Based on the information overload theory and knowledge content framework, we propose that the amount and types of feedback content have different effects on the likelihood of subsequent idea implementation, and such effects depend on the level of users' success experience. We tested the research model using a panel logistic model with the data of MIUI Forum. The study results revealed that the amount of feedback has an inverted U‐shaped effect on idea implementation, and such effect is moderated by a user's past success. Moreover, the type of feedback content (cost and benefit‐related feedback and functionality‐related feedback) positively affects idea implementation, and a user's past success positively moderated the above effects. Finally, we discuss the theoretical and practical implications, limitations of our research, and suggestions for future research.

The spread of misinformation on social media has become a major societal issue during recent years. In this work, we used the ongoing COVID-19 pandemic as a case study to systematically investigate factors associated with the spread of multi-topic misinformation related to one event on social media based on the heuristic-systematic model. Among factors related to systematic processing of information, we discovered that the topics of a misinformation story matter, with conspiracy theories being the most likely to be retweeted. As for factors related to heuristic processing of information, such as when citizens look up to their leaders during such a crisis, our results demonstrated that behaviors of a political leader, former US President Donald J. Trump, may have nudged people's sharing of COVID-19 misinformation. Outcomes of this study help social media platform and users better understand and prevent the spread of misinformation on social media.

Recent neural models for video captioning usually employ an attention-based encoder-decoder framework. However, current approaches mainly attend to the motion features and object features of the video when generating the caption, but ignore the potential but useful historical information. Besides, exposure bias and vanishing gradients problems always exist in current caption generation models. In this paper, we propose a novel video captioning framework, named Stacked Multimodal Attention Network (SMAN). It adopts additional visual and textual historical information during caption generation as context features, employs a stacked architecture to process different features gradually, and utilizes the Reinforcement Learning method and coarse-to-fine training strategy to further improve the generated results. Both quantitative and qualitative experiments on the benchmark datasets ofMSVDandMSR-VTTshow the effectiveness and feasibility of our framework. The codes are available onhttps://github.com/zhengyi123456/SMAN.

Abstract Project success is critical in the crowdfunding domain. Rather than the existing project‐centric prediction methods, we propose a novel backer‐centric prediction method. We identify each backer's preferences based on their pledge history and calculate the cosine similarity between backer's preferences and the project as each backer's persuasibility. Finally, we aggregate all the backers' persuasibility to predict project success. To validate our method, we crawled data on 183,886 projects launched during or before December 2014 on Kickstarter, a crowdfunding website. We selected 4,922 backers with a total of 442,793 pledges to identify backers' preferences. The results show that a backer is more likely to be persuaded by a project that is more similar to the backer's preferences. Our findings not only demonstrate the efficacy of backers' pledge history for predicting crowdfunding project success but also verify that a backer‐centric method can supplement the existing project‐centric approaches. Our model and findings enable crowdfunding platform agencies, fund‐seeking entrepreneurs, and investors to predict the success of a crowdfunding project.

Abstract In recent years, cross‐modal retrieval has been a popular research topic in both fields of computer vision and natural language processing. There is a huge semantic gap between different modalities on account of heterogeneous properties. How to establish the correlation among different modality data faces enormous challenges. In this work, we propose a novel end‐to‐end framework named Dual Multi‐Angle Self‐Attention (DMASA) for cross‐modal retrieval. Multiple self‐attention mechanisms are applied to extract fine‐grained features for both images and texts from different angles. We then integrate coarse‐grained and fine‐grained features into a multimodal embedding space, in which the similarity degrees between images and texts can be directly compared. Moreover, we propose a special multistage training strategy, in which the preceding stage can provide a good initial value for the succeeding stage and make our framework work better. Very promising experimental results over the state‐of‐the‐art methods can be achieved on three benchmark datasets of Flickr8k, Flickr30k, and MSCOCO.

How to integrate multimodal information sources for face naming in multimodal news is a hot and yet challenging problem. A novel deep cross-modal face naming scheme is developed in this paper to facilitate more effective people news retrieval for large-scale multimodal news. This scheme integrates deep multimodal analysis, cross-modal correlation learning, and multimodal information mining, in which the efficient naming mechanism aims to cluster the deep features of different modalities into a common space to explore their inter-related correlations, and a special Web mining pattern is designed to optimize the name-face matching for rare non-celebrity. Such a cross-modal face naming model can be treated as a problem of bi-media semantic mapping and modeled as an inter-related correlation distribution over deep representations of multimodal news, in which the most important is to create more effective cross-modal name-face correlation and measure to what degree they are correlated. The experiments on a large number of public data from Yahoo! News have obtained very positive results and demonstrated the effectiveness of the proposed model.

Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) has been proposed recently, putting the traditional Sketch-based Image Retrieval (SBIR) under the setting of zero-shot learning. Dealing with both the challenges in SBIR and zero-shot learning makes it become a more difficult task. Previous works mainly focus on utilizing one kind of information, i.e., the visual information or the semantic information. In this paper, we propose a SketchGCN model utilizing the graph convolution network, which simultaneously considers both the visual information and the semantic information. Thus, our model can effectively narrow the domain gap and transfer the knowledge. Furthermore, we generate the semantic information from the visual information using a Conditional Variational Autoencoder rather than only map them back from the visual space to the semantic space, which enhances the generalization ability of our model. Besides, feature loss, classification loss, and semantic loss are introduced to optimize our proposed SketchGCN model. Our model gets a good performance on the challenging Sketchy and TU-Berlin datasets.

Deep learning has made significant break through's in image classification, but accurate diagnosis on chest radiography remains challenging due to a variety of potential diseases contained in one scan. Complex relations among diseases have significant clinical meanings, but are always ignored in most of previous work. Thus in this paper, we propose a novel Label Generation Network (LGN) which treats the label sequence as the caption of a radiology image and utilizes RNN to generate the disease labels according to the semantic relations and co-occurrence dependency among them. However, the sequential generation process of RNN makes it hard to capture the complex topological relations among diseases. To mitigate this problem, a Historical Information Module (HIM) is especially introduced to LGN, in which all the generated labels are fully considered when generating a new label. Moreover, a specific self-attention mechanism is applied in HIM to learn the topological disease relations and utilize them to select useful historical information which can provide positive guidance to the prediction of new label. Very positive results have been obtained in our experiments on the benchmark dataset of Chest X-ray14, which significantly outperform the state-of-the-art methods.

Histopathological examination of tissue biopsies plays a fundamental role in disease assessment. Automatic histopathology image analysis requires substantial task-specific annotations, which are often expensive and laborious in realworld scenarios. This insufficient annotation of data limits the generalization ability of supervised learning models. To address this challenge, we propose a self-supervised Deformation Representation Learning (DRL) framework to learn semantic features from unlabeled data. As a novel paradigm, our approach utilizes deformation as supervisory signals based on two critical features, i.e., local structure heterogeneity and global context homogeneity. Given an original histopathology image and its deformed counterpart, there exists a moderate difference in local structures. In contrast, due to the transformation-invariance, both images share a similar global context compared with other images. Specifically, an encoder network is trained to distinguish the local inconsistency by measuring the mutual information and maintain the global consistency with noise contrastive estimation. Extensive experiments on public histopathology image datasets show that the learned representations are generalizable for various downstream tasks, such as transfer learning on segmentation and semi-supervised classification. Our approach achieves superior results over other self-supervised methods and the ImageNet pre-trained model, and it reveals the ability as a novel pre-training scheme in histopathology image analysis.

Video captioning aims to generate a natural language description for a given video clip that includes not only spatial information but also temporal information. To better exploit such spatial-temporal information attached to videos, we propose a novel video captioning framework with Temporal Graph Network (TGN) and Region Graph Network (RGN). TGN mainly focuses on utilizing the sequential information of frames that most of existing methods ignore. RGN is designed to explore the relationships among salient objects. Different from previous work, we introduce Graph Convolution Network (GCN) to encode frames with their sequential information and build a region graph for utilizing object information. We also particularly adopt a stack GRU decoder with a coarse-to-fine structure for caption generation. Very promising experimental results on two benchmark datasets (MSVD and MSR-VTT) show the effectiveness of our model.

Fueled by the explosive growth of Web 2.0 and social media, online investment communities have become a popular venue for individual investors to interact with each other. Investor opinions extracted from online investment communities capture “crowd wisdom” and have begun to play an important role in financial markets. Existing research confirms the importance of crowd wisdom in stock predictions, but fails to investigate factors influencing crowd performance (that is, crowd prediction accuracy). In order to help improve crowd performance, our research strives to investigate the impact of crowd characteristics on crowd performance. We conduct an empirical study using a large data set collected from a popular online investment community, StockTwits. Our findings show that experience diversity, participant independence, and network decentralization are all positively related to crowd performance. Furthermore, crowd size moderates the influence of crowd characteristics on crowd performance. From a theoretical perspective, our work enriches extant literature by empirically testing the relationship between crowd characteristics and crowd performance. From a practical perspective, our findings help investors better evaluate social sensors embedded in user‐generated stock predictions, based upon which they can make better investment decisions.

Abstract Online health question‐and‐answer consultation with physicians is becoming a common phenomenon. However, it is unclear how users identify the most satisfying reply. Based on the dual‐process theory of knowledge adoption, we developed a conceptual model and empirical method to study which factors influence adoption of a reply. We extracted 6 variables for argument quality (Ease of understanding, Relevance, Completeness, Objectivity, Timeliness, Structure) and 4 for source credibility (Physician's online experience, Physician's offline expertise, Hospital location, Hospital level). The empirical results indicate that both central and peripheral routes affect user's adoption of a response. Physician's offline expertise negatively affects user's adoption decision, while physician's online experience positively affects it; this effect is positively moderated by user involvement.

Visual and semantic saliency are important in image captioning. However, single-phase image captioning benefits little from limited saliency without a saliency predictor. In this paper, a novel saliency-enhanced re-captioning framework via two-phase learning is proposed to enhance the single-phase image captioning. In the framework, visual saliency and semantic saliency are distilled from the first-phase model and fused with the second-phase model for model self-boosting. The visual saliency mechanism can generate a saliency map and a saliency mask for an image without learning a saliency map predictor. The semantic saliency mechanism sheds some lights on the properties of words with part-of-speech Noun in a caption. Besides, another type of saliency, sample saliency is proposed to explicitly compute the saliency degree of each sample, which helps for more robust image captioning. In addition, how to combine the above three types of saliency for further performance boost is also examined. Our framework can treat an image captioning model as a saliency extractor, which may benefit other captioning models and related tasks. The experimental results on both the Flickr30k and MSCOCO datasets show that the saliency-enhanced models can obtain promising performance gains.

Automatically generating natural language description for video is an extremely complicated and challenging task. To tackle the obstacles of traditional LSTM-based model for video captioning, we propose a novel architecture to generate the optimal descriptions for videos, which focuses on constructing a new network structure that can generate sentences superior to the basic model with LSTM, and establishing special attention mechanisms that can provide more useful visual information for caption generation. This scheme discards the traditional LSTM, and exploits the fully convolutional network with coarse-to-fine and inherited attention designed according to the characteristics of fully convolutional structure. Our model cannot only outperform the basic LSTM-based model, but also achieve the comparable performance with those of state-of-the-art methods

The recent increase in online user generated content (UGC) has led to the availability of a large number of posts about products and services. Often, these posts contain complaints that the consumers purchasing the products and services have. However, discovering and summarizing product defects and the related knowledge from large quantities of user posts is a difficult task. Traditional aspect opinion mining models, that aim to discover the product aspects and their corresponding opinions, are not sufficient to discover the product defect information from the user posts. In this paper, we propose the Product Defect Latent Dirichlet Allocation model (PDLDA), a probabilistic model that identifies domain-specific knowledge about product issues using interdependent three-dimensional topics: Component, Symptom, and Resolution. A Gibbs sampling based inference method for PDLDA is also introduced. To evaluate our model, we introduce three novel product review datasets. Both qualitative and quantitative evaluations show that the proposed model results in apparent improvement in the quality of discovered product defect information. Our model has the potential to benefit customers, manufacturers, and policy makers, by automatically discovering product defects from online data.

In this paper, we investigated the customer churn prediction problem in the Internet funds industry. We designed a novel feature embedded convolutional neural networks (FE-CNN) method that can automatically learn features from both the dynamic customer behavioral data and static customer demographic data and can utilize the advantage of convolutional neural networks to automatically learn features that capture the structured information. Our results show that our FE-CNN model outperforms the other traditional machine learning models with hand-crafted features, such as logistic regression (LR), support vector machines (SVM), random forests (RF) and neural networks (NN) in terms of accuracy, area under the receiver operating characteristics curve (AUC) and top-decile lift. Furthermore, we found that after adding the demographic data feature to the basic CNN model, the performance of the FE-CNN model improved. Overall, we found that the FE-CNN is the most powerful way to solve the problem of customer churn prediction in the Internet funds industry. Our FE-CNN method can also be applied to other fields that have both dynamic data and static data.

Search engines are mostly targeted toward the generic user but educators have specific information needs with specialized information-seeking behaviors. When designing a course, educators may create a syllabus, lecture slides, and use tools as lecture aides. These resources are scattered across a large number of websites and require time and effort in finding them. To design a useful system for educators we must understand the problems they face when they seek, use, and re-use online resources. With that in mind, the Ensemble team conducted focus groups to identify current practices and problems in locating online resources for educational purposes. The data provides better understanding of the information-seeking process of educators that can lead to better educational resource sites.

Dimension reduction for large-scale text data is attracting much attention nowadays due to the rapid growth of the World Wide Web. We can categorize those popular dimension reduction algorithms into two groups: feature extraction and feature selection algorithms. In the former, new features are combined from their original features through algebraic transformation. Though many of them have been validated to be effective, these algorithms are typically associated with high computational overhead, making them difficult to be applied on real-world text data. In the latter, subsets of features are selected directly. These algorithms are widely used in real-world tasks owing to their efficiency, but are often based on greedy strategies rather than optimal solutions. An important problem remains: it has been troublesome to integrate these two types of algorithms into a single framework, making it difficult to reap the benefits from both. In this paper, we formulate the two algorithm categories through a unified optimization framework, under which we develop a novel feature selection algorithm called Trace-Oriented Feature Analysis (TOFA). In detail, we integrate the objective functions of several state-of-the-art feature extraction algorithms into a unified one under the optimization framework, and then we propose to optimize this objective function in the solution space of feature selection algorithms for dimensionality reduction. Since the proposed objective function of TOFA integrates many prominent feature extraction algorithms' objective functions, such as unsupervised Principal Component Analysis (PCA) and supervised Maximum Margin Criterion (MMC), TOFA can handle both supervised and unsupervised problems. In addition, by tuning a weight value, TOFA is also suitable to solve semisupervised learning problems. Experimental results on several real-world data sets validate the effectiveness and efficiency of TOFA in text data for dimensionality reduction purpose.

E-Commerce has shown its exponentially-growing business value in the past decade. However, in contrast to the successful examples in online sales, such as Amazon1 and eBay2, the online barter business is still underexplored due to the lack of corresponding information aggregation service. In this paper, we design and implement a novel vertical search engine, called ExSearch, to aggregate online barter information for developing the barter market. Different from classical general purpose Web search engines, ExSearch adopts a focused crawler to gather related information from various websites. We propose to automatically extract the barter information from free-text Web pages such that the unstructured information is represented in structured databases. In addition, we utilize the data mining techniques such as regression to fulfill the missing information, which cannot be extracted from the Web pages. Finally, we validate and rank the search results according to user queries. Experimental results show that each component module in our proposed ExSearch system is efficient and effective. The volunteer users are satisfied by and interested in this novel vertical search engine.

A pressing task during the unification process is to identify a user's vertical search intention based on the user's query. In this paper, we propose a novel method to propagate social annotation, which includes user-supplied tag data, to both queries and VSEs for semantically bridging them. Our proposed algorithm consists of three key steps: query annotation, vertical annotation and query intention identification. Our algorithm, referred to as TagQV, verifies that the social tagging can be propagated to represent Web objects such as queries and VSEs besides Web pages. Experiments on real Web search queries demonstrate the effectiveness of TagQV in query intention identification.

There is an increasing number of people reading, writing, and commenting on blogs. According to a recent survey made by Technorati, there are about 75,000 new blogs and 1.2 million new posts everyday. However, it is difficult and time consuming for a blog reader to find the most interesting posts in the huge and dynamic blog world. In this article, an online Personalized Blog Reader (PBR) system is proposed, which facilitates blog readers in browsing the coolest and newest blog posts of their interests by automatically clustering the most relevant stories. PBR aims to make a user's potential favorite topics always ranked higher than those nonfavorite ones. This is accomplished in the following steps. First, the system collects and provides a unified incremental index of posts coming from different blogs. Then, an incremental clustering algorithm with a flexible half-bounded window of observation is proposed to satisfy the requirements of online processing. It learns people's personalized reading preferences to present a user with a final reading list. The experimental results show that the proposed incremental clustering algorithm is effective and efficient, and the personalization of the PBR performs well.

Dimension reduction for large-scale text data is attracting much attention lately due to the rapid growth of World Wide Web. We can consider dimension reduction algorithms in two categories: feature extraction and feature selection. An important problem remains: it has been difficult to integrate these two algorithm categories into a single framework, making it difficult to reap the benefit of both. In this paper, we formulate the two algorithm categories through a unified optimization framework. Under this framework, we develop a novel feature selection algorithm called Trace Oriented Feature Analysis (TOFA). The novel objective function of TOFA is a unified framework that integrates many prominent feature extraction algorithms such as unsupervised Principal Component Analysis and supervised Maximum Margin Criterion are special cases of it. Thus TOFA can process not only supervised problem but also unsupervised and semi-supervised problems. Experimental results on real text datasets demonstrate the effectiveness and efficiency of TOFA.

A syllabus defines the contents of a course, as well as other information such as resources and assignments. In this paper, we report on our work towards creating a syllabus repository of Computer Science courses across universities in the USA. We present some statistics from our initial collection of 8000+ syllabi. We show a syllabus creator that is integrated with Moodle [5] moodle_2006_moodle, an open-source course management system, which allows for the creation of a syllabus for a particular course. Among other information, it includes knowledge units from the Computing Curricula 2001 body of knowledge. The goal of the syllabus repository is to provide added value to the Computer Science Education community, and we present some such offerings. We conclude by presenting our future plans for the syllabus repository. These include using automated techniques to collect and classify syllabi, providing recommendations to instructors when creating a syllabus, and allowing the community to share their syllabi automatically. The syllabus collection will be part of the Computing and Information Technology Interactive Digital Educational Library (CITIDEL), a collection of the National Science Digital Library (NSDL).

In recent years, different commercial Weblog subscribing systems have been proposed to return stories from users. subscribed feeds. In this paper, we propose a novel clustering-based RSS aggregator called as RSS Clusgator System (RCS) for Weblog reading. Note that an RSS feed may have several different topics. A user may only be interested in a subset of these topics. In addition there could be many different stories from multiple RSS feeds, which discuss similar topic from different perspectives. A user may be interested in this topic but do not know how to collect all feeds related to this topic. In contrast to many previous works, we cluster all stories in RSS feeds into hierarchical structure to better serve the readers. Through this way, users can easily find all their interested stories. To make the system current, we propose a flexible time window for incremental clustering. RCS utilizes both link information and content information for efficient clustering. Experiments show the effectiveness of RCS.

Content-targeted advertising, the task of automatically associating ads to a Web page, constitutes a key Web monetization strategy nowadays. Further, it introduces new challenging technical problems and raises interesting questions. For instance, how to design ranking functions able to satisfy conflicting goals such as selecting advertisements (ads) that are relevant to the users and suitable and profitable to the publishers and advertisers? In this paper we propose a new framework for associating ads with web pages based on Genetic Programming (GP). Our GP method aims at learning functions that select the most appropriate ads, given the contents of a Web page. These ranking functions are designed to optimize overall precision and minimize the number of misplacements. By using a real ad collection and web pages from a newspaper, we obtained a gain over a state-of-the-art baseline method of 61.7% in average precision. Further, by evolving individuals to provide good ranking estimations, GP was able to discover ranking functions that are very effective in placing ads in web pages while avoiding irrelevant ones.

Dimensionality reduction is an essential data preprocessing technique for large-scale and streaming data classification tasks. It can be used to improve both the efficiency and the effectiveness of classifiers. Traditional dimensionality reduction approaches fall into two categories: feature extraction and feature selection. Techniques in the feature extraction category are typically more effective than those in feature selection category. However, they may break down when processing large-scale data sets or data streams due to their high computational complexities. Similarly, the solutions provided by the feature selection approaches are mostly solved by greedy strategies and, hence, are not ensured to be optimal according to optimized criteria. In this paper, we give an overview of the popularly used feature extraction and selection algorithms under a unified framework. Moreover, we propose two novel dimensionality reduction algorithms based on the orthogonal centroid algorithm (OC). The first is an incremental OC (IOC) algorithm for feature extraction. The second algorithm is an orthogonal centroid feature selection (OCFS) method which can provide optimal solutions according to the OC criterion. Both are designed under the same optimization criterion. Experiments on Reuters Corpus Volume-1 data set and some public large-scale text data sets indicate that the two algorithms are favorable in terms of their effectiveness and efficiency when compared with other state-of-the-art algorithms.

We present the architecture of our web question answering (fact seeking) system and introduce a novel algorithm to validate semantic categories of the expected answers. When tested on the questions used by the prior research, our system demonstrated the performance comparable to the current state of the art systems. Our semantic verification algorithm has improved the accuracy of answers of the affected questions by 30%.

We have designed a representation scheme, which is based on the discrete representation of a document ranking function, which is capable of reproducing and enhancing the properties of such popular ranking functions as tf.idf, BM25 or those based on language models. Our tests have demonstrated the capability of our approach to achieve the performance of the best known scoring functions solely through training, without using any known heuristic or analytic formulas.

In this paper, we propose a novel framework using Genetic Programming to combine image database descriptors for content-based image retrieval (CBIR). Our framework is validated through several experiments involving two image databases and specific domains, where the images are retrieved based on the shape of their objects.

This paper shows how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity -- five derived from the citation information of the collection, and three derived from the structural content -- and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than GA or other fusion techniques.

In this paper we use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects (e.g., web pages, queries) and their interrelationships (e.g., hyperlinks, user click-through sequences). We claim that iterative computations over the URM can help overcome the data sparseness problem and detect latent relationships among heterogeneous data objects, thus, can improve the quality of information applications that require com- bination of information from heterogeneous sources. To support our claim, we present a unified similarity-calculating algorithm, SimFusion. By iteratively computing over the URM, SimFusion can effectively integrate relationships from heterogeneous sources when measuring the similarity of two data objects. Experiments based on a web search engine query log and a web page collection demonstrate that SimFusion can improve similarity measurement of web objects over both traditional content based algorithms and the cutting edge SimRank algorithm.

Text categorization is an important research area in many Information Retrieval (IR) applications. To save the storage space and computation time in text categorization, efficient and effective algorithms for reducing the data before analysis are highly desired. Traditional techniques for this purpose can generally be classified into feature extraction and feature selection. Because of efficiency, the latter is more suitable for text data such as web documents. However, many popular feature selection techniques such as Information Gain (IG) andχ2-test (CHI) are all greedy in nature and thus may not be optimal according to some criterion. Moreover, the performance of these greedy methods may be deteriorated when the reserved data dimension is extremely low. In this paper, we propose an efficient optimal feature selection algorithm by optimizing the objective function of Orthogonal Centroid (OC) subspace learning algorithm in a discrete solution space, called Orthogonal Centroid Feature Selection (OCFS). Experiments on 20 Newsgroups (20NG), Reuters Corpus Volume 1 (RCV1) and Open Directory Project (ODP) data show that OCFS is consistently better than IG and CHI with smaller computation time especially when the reduced dimension is extremely small.

This paper shows how different measures of similarity derived from the citation information and the structural content (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than other fusion techniques.

In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.

Abstract Web‐based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that augments existing search engines so that they support natural language question answering. The process entails five steps: query modulation, document retrieval, passage extraction, phrase extraction, and answer ranking. In this article, we describe some probabilistic approaches to the last three of these stages. We show how our techniques apply to a number of existing search engines, and we also present results contrasting three different methods for question answering. Our algorithm, probabilistic phrase reranking (PPR), uses proximity and question type features and achieves a total reciprocal document rank of .20 on the TREC8 corpus. Our techniques have been implemented as a Web‐accessible system, called NSIR.

The performance of web search engines may often deteriorate due to the diversity and noisy information contained within web pages. User click-through data can be used to introduce more accurate description (metadata) for web pages, and to improve the search performance. However, noise and incompleteness, sparseness, and the volatility of web pages and are three major challenges for research work on user click-through log mining. In this paper, we propose a novel iterative reinforced algorithm to utilize the user click-through data to improve search performance. The algorithm fully explores the interrelations between and web pages, and effectively finds virtual queries for web pages and overcomes the challenges discussed above. Experiment results on a large set of MSN click-through log data show a significant improvement on search performance over the naive query log mining algorithm as well as the baseline search engine.

This paper discusses how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity derived from the citation structure and the structural content of the collection, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM Digital Library and the ACM Computing Classification System show that we can discover similarity functions that work better than using evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers.

Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or large-scale data. To meet this desirability, Incremental Principal Component Analysis (IPCA) algorithm has been well established, but it is an unsupervised subspace learning approach and is not optimal for general classification tasks, such as face recognition and Web document categorization. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Maximum Margin Criterion (IMMC), to infer an adaptive subspace by optimizing the Maximum Margin Criterion. We also present the proof for convergence of the proposed algorithm. Experimental results on both synthetic dataset and real world datasets show that IMMC converges to the similar subspace as that of batch approach.

Both ranking functions and user queries are very important factors affecting a search engine's performance. Prior research has looked at how to improve ad-hoc retrieval performance for existing queries while tuning the ranking function, or modify and expand user queries using a fixed ranking scheme using blind feedback. However, almost no research has looked at how to combine ranking function tuning and blind feedback together to improve ad-hoc retrieval performance. In this paper, we look at the performance improvement for ad-hoc retrieval from a more integrated point of view by combining the merits of both techniques. In particular, we argue that the ranking function should be tuned first, using user-provided queries, before applying the blind feedback technique. The intuition is that highly-tuned ranking offers more high quality documents at the top of the hit list, thus offers a stronger baseline for blind feedback. We verify this integrated model in a large scale heterogeneous collection and the experimental results show that combining ranking function tuning and blind feedback can improve search performance by almost 30% over the baseline Okapi system.

Abstract Genetic‐based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR task—discovery of ranking functions for Web search—and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP‐based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is well known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs on GP‐based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations on the design of fitness functions for genetic‐based information retrieval experiments.

The Internet and corporate intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. We argue that the ranking strategy should be context specific and we propose a , new systematic method that can automatically generate ranking strategies for different contexts based on genetic programming (GP). The new method was tested on TREC data and the results are very promising.

Web-based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that augments existing search engines so that they support natural language question answering. The process entails five steps: query modulation, document retrieval, passage extraction, phrase extraction, and answer ranking. In this paper we describe some probabilistic approaches to the last three of these stages. We show how our techniques apply to a number of existing search engines and we also present results contrasting three different methods for question answering. Our algorithm, probabilistic phrase reranking (PPR) using proximity and question type features achieves a total reciprocal document rank of .20 on the TREC 8 corpus. Our techniques have been implemented as a Web-accessible system, called NSIR.

Abstract Most popular search engines are not designed for answering natural language questions. However, when we asked hundreds of natural language questions of nine leading search engines, all retrieved at least one correct answer on more than three‐quarters of the questions. We identified the best‐performing search engines overall for factual natural language questions. We found performance differences depending on the domain of factual question asked. Other aspects of questions also predicted significantly different performance: the number of words in the question, the presence of a proper noun, and whether the question is time dependent. An additional analysis tested for differential performance by specific search engines on these four question factors. The analysis found no evidence for such interactions.

Previous research has shown that researchers can generate medical hypotheses by using computers to analyze several, seemingly unrelated, medical literatures. In this work we suggest broader application for the ideas of literature-based discovery. Specifically, we suggest that literature-based discovery can be fruitful in areas other than medicine; that in addition to finding "cures" for "problems," literature-based discovery offers the possibility of finding new problems for existing technologies; that the analysis of a single literature may be sufficient for literature-based discovery; and that literature-based discovery can support individuals seeking to draw together ideas from various areas of inquiry, even if such connections have been previously made by others.We describe literature-based discovery experiments conducted on the World Wide Web that support these ideas.

The web is now becoming one of the largest information and knowledge repositories. Many large scale search engines (Google, Fast, Northern Light, etc.) have emerged to help users find information. In this paper, we study how we can effectively use these existing search engines to mine the Web and discover the "correct" answers to factual natural language questions.We propose a probabilistic algorithm called QASM (Question Answering using Statistical Models) that learns the best query paraphrase of a natural language question. We validate our approach for both local and web search engines using questions from the TREC evaluation. We also show how this algorithm can be combined with another algorithm (AnSel) to produce precise answers to natural language questions.