Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. (Advanced Information and Knowledge Processing)

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

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Handbook of software reliability engineering

Web search engines help users find useful information on the World Wide Web (WWW). However, when the same query is submitted by different users, typical search engines return the same result regardless of who submitted the query. Generally, each user has different information needs for his/her query. Therefore, the search result should be adapted to users with different information needs. In this paper, we first propose several approaches to adapting search results according to each user's need for relevant information without any user effort, and then verify the effectiveness of our proposed approaches. Experimental results show that search systems that adapt to each user's preferences can be achieved by constructing user profiles based on modified collaborative filtering with detailed analysis of user's browsing history in one day.

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Adaptive web search based on user profile constructed without any effort from users

Online news reading has become very popular as the web provides access to news articles from millions of sources around the world. A key challenge of news websites is to help users find the articles that are interesting to read. In this paper, we present our research on developing personalized news recommendation system in Google News. For users who are logged in and have explicitly enabled web history, the recommendation system builds profiles of users' news interests based on their past click behavior. To understand how users' news interests change over time, we first conducted a large-scale analysis of anonymized Google News users click logs. Based on the log analysis, we developed a Bayesian framework for predicting users' current news interests from the activities of that particular user and the news trends demonstrated in the activity of all users. We combine the content-based recommendation mechanism which uses learned user profiles with an existing collaborative filtering mechanism to generate personalized news recommendations. The hybrid recommender system was deployed in Google News. Experiments on the live traffic of Google News website demonstrated that the hybrid method improves the quality of news recommendation and increases traffic to the site.

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Personalized news recommendation based on click behavior

No health without mental health.

Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.

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CARER: Contextualized Affect Representations for Emotion Recognition

Recommendation systems are applied to personalize and customize the Web environment. We have developed a recommendation system, termed Yoda, that is designed to support large-scale Web-based applications requiring highly accurate recommendations in real-time. With Yoda, we introduce a hybrid approach that combines collaborative filtering (CF) and content-based querying to achieve higher accuracy. Yoda is structured as a tunable model that is trained off-line and employed for real-time recommendation on-line. The on-line process benefits from an optimized aggregation function with low complexity that allows realtime weighted aggregation of the soft classification of active users to predefined recommendation sets. Leveraging on localized distribution of the recommendable items, the same aggregation function is further optimized for the off-line process to reduce the time complexity of constructing the pre-defined recommendation sets of the model. To make the off-line process scalable furthermore, we also propose a filtering mechanism, FLSH, that extends the Locality Sensitive Hashing technique by incorporating a novel distance measure that satisfies specific requirements of our application. Our end-to-end experiments show while Yoda's complexity is low and remains constant as the number of users and/or items grow, its accuracy surpasses that of the basic nearest-neighbor method by a wide margin (in most cases more than 100%).

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Yoda: An Accurate and Scalable Web-Based Recommendation System

Recommendation systems are widely adopted in e-commerce businesses for helping customers locate products they would like to purchase. In an earlier work, we introduced a recommendation system, termed Yoda, which employs a hybrid approach that combines collaborative filtering (CF) and content-based querying to achieve higher accuracy for large-scale Web-based applications. To reduce the complexity of the hybrid approach, Yoda is structured as a tunable model that is trained off-line and employed for real-time recommendation on-line. The on-line process benefits from an optimized aggregation function with low complexity that allows the real-time aggregation based on confidence values of an active user to pre-defined sets of recommendations. In this paper, we extend Yoda to include more recommendation sets. The recommendation sets can be obtained from different sources, such as human experts, web navigation patterns, and clusters of user evaluations. Moreover, the extended Yoda can learn the confidence values automatically by utilizing implicit users' relevance feedback through web navigations using genetic algorithms (GA). Our end-to-end experiments show while Yoda's complexity is low and remains constant as the number of users and/or items grow, its accuracy surpasses that of the basic nearest-neighbor method by a wide margin (in most cases more than 100%). The experimental results also indicate that the retrieval accuracy is significantly increased by using the GA-based learning mechanism.

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An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback

As the number of web pages increases dramatically, the problem of the information overload becomes more severe when browsing and searching the WWW. To alleviate this problem, personalization becomes a popular remedy to customize the Web environment towards a user’s preference. To date, recommendation systems and personalized web search systems are the most successful examples of Web personalization. By focusing on these two types of systems, this paper reviews the challenges and the corresponding approaches proposed in the past ten years.

Web Information Personalization: Challenges and Approaches

Mental illnesses rank as some of the most disabling conditions, affecting millions of people, across the globe. In general, the main challenge of mental disorders is that they remain difficult to detect on suffering patients. In an online environment, the challenge extends to the collection of patients data and the implementation of proper algorithms to assist in the detection of such illnesses. In this paper, we propose a novel data collection mechanism and build predictive models that leverage language and behavioral patterns, used particularly on Twitter, to determine whether a user is suffering from a mental disorder. After training the predictive models, they are further pre-trained to serve as the back-end for our demonstration, MIDAS. MIDAS offers an analytics web-service to explore several characteristics pertaining to user's linguistic and behavioral patterns on social media, with respect to mental illnesses.

Yi-Shin Chen

Papers

CARER: Contextualized Affect Representations for Emotion Recognition

Yoda: An Accurate and Scalable Web-Based Recommendation System

An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback

Web Information Personalization: Challenges and Approaches

MIDAS: mental illness detection and analysis via social media