An individual’s ability to locate the information they desire grows more slowly than the rate at which new information becomes available. Customers are constantly confronted with situations in which they have many options to choose from and need assistance exploring or narrowing down the possibilities. Recommender systems are one tool to help bridge this gap. There are various mechanisms being employed to create recommender systems, but the most common systems fall into two main classes: content-based and collaborative filtering systems. Content-based recommender systems match the textual information of a particular product with the textual information representing the interests of a customer. Collaborative filtering systems use patterns in customer ratings to make recommendations. Both types of recommender systems require significant data resources in the form of a customer’s ratings and product features; hence they are not able to generate high quality recommendations. Hybrid mechanisms have been used by researchers to improve the performance of recommender systems where one can integrate more than one mechanism to overcome the drawbacks of an individual system. The hybrid approach proposed in this thesis is the integration of content and context-based with collaborative filtering, since these are the most successful and widely used mechanisms. This proposed approach will look into the integration of content and context data with rating data using a different mechanism that mainly focuses on boosting a customer’s trust in the recommender system. Researchers have been trying to improve system performance using hybrid approaches, but research is lacking on providing justifications for recommended products. Hence, the proposed approach will mainly focus on providing justifications for recommended products as this plays a crucial role in obtaining the satisfaction and trust of customers. A product’s features and a customer’s context attributes are used to provide justifications. In addition to this, the presentation mechanism needs to be very effective as it has been observed that customers trust more in a system when there are explanations on how the recommended products have been computed and presented. Finally, this proposed recommender system will allow the customer to interact with it in various ways to provide feedback on the recommendations and justifications. Overall, this integration will be very useful in achieving a stronger correlation between the customers and products. Experimental results clearly showed that the majority of the participants prefer to have recommendations with their justifications and they received valuable recommendations on which they could trust.

This research investigated issues related to normalizing punctuation marks from a text retrieval perspective. A punctuated-centric approach was undertaken by exploring changes in meanings, whitespaces, words retrievability, and other issues related to normalizing punctuation marks. To investigate punctuation normalization issues, various frequency counts of punctuation marks and punctuation patterns were conducted using the text drawn from the Gutenberg Project archive and the Usenet Newsgroup archive. A number of useful punctuation mark types that could aid in analyzing punctuation marks were discovered. This study identified two types of punctuation normalization procedures: (1) lexical independent (LI) punctuation normalization and (2) lexical oriented (LO) punctuation normalization. Using these two types of punctuation normalization procedures, this study discovered various effects of punctuation normalization in terms of different search query types. By analyzing the punctuation normalization problem in this manner, a wide range of issues were discovered such as: the need to define different types of searching, to disambiguate the role of punctuation marks, to normalize whitespaces, and indexing of punctuated terms. This study concluded that to achieve the most positive effect in a text retrieval environment, normalizing punctuation marks should be based on an extensive systematic analysis of punctuation marks and punctuation patterns and their related factors. The results of this study indicate that there were many challenges due to complexity of language. Further, this study recommends avoiding a simplistic approach to punctuation normalization.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19852号 / 情博第603号 / 新制||情||105(附属図書館) / 32888 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 田中 克己, 教授 石田 亨, 教授 吉川 正俊 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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Traceability is defined as the ability to establish, record, and maintain dependency relations among various software artifacts in a software system, in both a forwards and backwards direction, throughout the multiple phases of the project’s life cycle. The availability of traceability information has been proven vital to several software engineering activities such as program comprehension, impact analysis, feature location, software reuse, and verification and validation (V&V). The research on automated software traceability has noticeably advanced in the past few years. Various methodologies and tools have been proposed in the literature to provide automatic support for establishing and maintaining traceability information in software systems. This movement is motivated by the increasing attention traceability has been receiving as a critical element of any rigorous software development process. However, despite these major advances, traceability implementation and use is still not pervasive in industry. In particular, traceability tools are still far from achieving performance levels that are adequate for practical applications. Such low levels of accuracy require software engineers working with traceability tools to spend a considerable amount of their time verifying the generated traceability information, a process that is often described as tedious, exhaustive, and error-prone. Motivated by these observations, and building upon a growing body of work in this area, in this dissertation we explore several research directions related to enhancing the performance of automated tracing tools and techniques. In particular, our work addresses several issues related to the various aspects of the IR-based automated tracing process, including trace link retrieval, performance enhancement, and the role of the human in the process. Our main objective is to achieve performance levels, in terms of accuracy, efficiency, and usability, that are adequate for practical applications, and ultimately to accomplish a successful technology transfer from research to industry.

No description available.

The amount of information available on both public and private networks continues to grow at a phenomenal rate. This information is contained within a wide variety of objects, including documents, e-mail archives, medical records, manuals, pictures and music. To be of any value, this data must be easily searchable and accessible. Information Retrieval (IR) is concerned with the ability to find and gain access to relevant information. As electronic data repositories continue to proliferate, so too, grows the variety of methods used to locate and access the information contained therein. Similarly, the introduction of innovative retrieval strategies—and the optimization of older strategies—emphasizes the need for an infrastructure capable of measuring and comparing the performance of competing Information Retrieval solutions, but such an environment does not yet exist. The purpose of this research is to develop an infrastructure wherein Information Retrieval solutions may be evaluated and compared. In 1979, an expert in the field believed the need for a system-independent benchmarking utility was long overdue—twenty-five years later, progress in this area has been minimal. Contrastingly, new theories have emerged; new techniques have been introduced; all with the goal of improving retrieval performance. The need for a system-independent analysis of retrieval performance is more critical now.

The rapid production of digital information makes the task of locating relevant information increasingly difficult. Keyword search alleviates this difficulty by retrieving documents containing keywords of interest. However, keyword search suffers from a number of issues such ambiguity, synonymy, and the inability to handle semantic constraints. Semantic search helps resolve these issues but is limited by the quality of annotations which are likely to be incomplete or imprecise. Hybrid search, a search technique that combines the merits of both keyword and semantic search, appears to be a promising solution. In this work we introduce HyKSS, a hybrid search system driven by extraction ontologies for both annotation creation and query interpretation. HyKSS is not limited to a single domain, but rather allows queries to cross ontological boundaries. We show that our hybrid search system, which uses a query driven dynamic ranking mechanism, outperforms keyword and semantic search in isolation, as well as a number of other non-HyKSS hybrid ranking approaches, over data sets of short topical documents. We also find that there is not a statistically significant difference between using multiple ontologies for query generation and simply selecting and using the best matching ontology.

A growing number of information retrieval applications rely on search behavior aggregated over many users. If aggregated data such as search query reformulations is not handled properly, it can allow users to be identified and their privacy compromised. Besides leveraging aggregate data, it is also common for applications to make use of user-specific behavior in order to provide a personalized experience for users. Unlike aggregate data, privacy is not an issue in individual personalization since users are the only consumers of their own data. The goal of this work is to explore the effects of personalization and privacy preservation methods on three information retrieval applications, namely search task identification, task-aware query recommendation, and searcher frustration detection. We pursue this goal by first introducing a novel framework called CrowdLogging for logging and aggregating data privately over a distributed set of users. We then describe several privacy mechanisms for sanitizing global data, including one novel mechanism based on differential privacy. We present a template for describing how local user data and global aggregate data are collected, processed, and used within an application, and apply this template to our three applications. We find that sanitizing feature vectors aggregated across users has a low impact on performance for classification applications (search task identification and searcher frustration detection). However, sanitizing free-text query reformulations is extremely detrimental to performance for the query recommendation application we consider. Personalization is useful to some degree in all the applications we explore when integrated with global information, achieving gains for search task identification, task-aware query recommendation, and searcher frustration detection. Finally we introduce an open source system called CrowdLogger that implements the CrowdLogging framework and also serves as a platform for conducting in-situ user studies of search behavior, prototyping and evaluating information retrieval applications, and collecting labeled data.

The main goal of this thesis is to investigate query-dependent selection of retrieval alternatives for Information Retrieval (IR) systems. Retrieval alternatives include choices in representing queries (query representations), and choices in methods used for scoring documents. For example, an IR system can represent a user query without any modification, automatically expand it to include more terms, or reduce it by dropping some terms. The main motivation for this work is that no single query representation or retrieval model performs the best for all queries. This suggests that selecting the best representation or retrieval model for each query can yield improved performance. The key research question in selecting between alternatives is how to estimate the performance of the different alternatives. We treat query dependent selection as a general problem of selecting between the result sets of different alternatives. We develop a relative effectiveness estimation technique using retrieval-based features and a learning formulation that directly predict differences between the results sets. The main idea behind this technique is to aggregate the scores and features used for retrieval (retrieval-based features) as evidence towards the effectiveness of the results set. We apply this general technique to select between alternatives reduced versions for long queries and to combine multiple ranking algorithms. Then, we investigate the extension of query-dependent selection under specific efficiency constraints. Specifically, we consider the black-box meta-search scenario, where querying all available search engines can be expensive and the features and scores used by the search engines are not available. We develop easy-to-compute features based on the results page alone to predict when querying an alternate search engine can be useful. Finally, we present an analysis of selection performance to better understand when query-dependent selection can be useful.