Intent-aware search result diversification

Search result diversification has gained momentum as a way to tackle ambiguous queries. An effective approach to this problem is to explicitly model the possible aspects underlying a query, in order to maximise the estimated relevance of the retrieved documents with respect to the different aspects. However, such aspects themselves may represent information needs with rather distinct intents (e.g., informational or navigational). Hence, a diverse ranking could benefit from applying intent-aware retrieval models when estimating the relevance of documents to different aspects. In this paper, we propose to diversify the results retrieved for a given query, by learning the appropriateness of different retrieval models for each of the aspects underlying this query. Thorough experiments within the evaluation framework provided by the diversity task of the TREC 2009 and 2010 Web tracks show that the proposed approach can significantly improve state-of-the-art diversification approaches

Intelligent personalized approaches for semantic search and query expansion

University of Technology Sydney. Faculty of Engineering and Information Technology.In today’s highly advanced technological world, the Internet has taken over all aspects of human life. Many services are advertised and provided to the users through online channels. The user looks for services and obtains them through different search engines. To obtain the best results that meet the needs and requirements of the users, researchers have extensively studied methods such as different personalization methods by which to improve the performance and efficiency of the retrieval process. A key part of the personalization process is the generation of user models. The most commonly used user models are still rather simplistic, representing the user as a vector of ratings or using a set of keywords. Recently, semantic techniques have had a significant importance in the field of personalized querying and personalized web search engines. This thesis focuses on both processes of personalized web search engines, first the reformulation of queries and second ranking query results. The importance of personalized web search lies in its ability to identify users' interests based on their personal profiles. This work contributes to personalized web search services in three aspects. These contributions can be summarized as follows: First, it creates user profiles based on a user’s browsing behaviour, as well as the semantic knowledge of a domain ontology, aiming to improve the quality of the search results. However, it is not easy to acquire personalized web search results, hence one of the problems that is encountered in this approach is how to get a precise representation of the user interests, as well as how to use it to find search results. The second contribution builds on the first contribution. A personalized web search approach is introduced by integrating user context history into the information retrieval process. This integration process aims to provide search results that meet the user’s needs. It also aims to create contextual profiles for the user based on several basic factors: user temporal behaviour during browsing, semantic knowledge of a specific domain ontology, as well as an algorithm based on re-ranking the search results. The previous solutions were related to the re-ranking of the returned search results to match the user’s requirements. The third contribution includes a comparison of three-term weight methods in personalized query expansion. This model has been built to incorporate both latent semantics and weighting terms. Experiments conducted in the real world to evaluate the proposed personalized web search approach; show promising results in the quality of reformulation and re-ranking processes compared to Google engine techniques

Modelling users' contextual querying behaviour for web image searching

The rapid growth of visual information on Web has led to immense interest in multimedia information retrieval (MIR). While advancement in MIR systems has achieved some success in specific domains, particularly the content-based approaches, general Web users still struggle to find the images they want. Despite the success in content-based object recognition or concept extraction, the major problem in current Web image searching remains in the querying process. Since most online users only express their needs in semantic terms or objects, systems that utilize visual features (e.g., color or texture) to search images create a semantic gap which hinders general users from fully expressing their needs. In addition, query-by-example (QBE) retrieval imposes extra obstacles for exploratory search because users may not always have the representative image at hand or in mind when starting a search (i.e. the page zero problem). As a result, the majority of current online image search engines (e.g., Google, Yahoo, and Flickr) still primarily use textual queries to search. The problem with query-based retrieval systems is that they only capture users’ information need in terms of formal queries;; the implicit and abstract parts of users’ information needs are inevitably overlooked. Hence, users often struggle to formulate queries that best represent their needs, and some compromises have to be made. Studies of Web search logs suggest that multimedia searches are more difficult than textual Web searches, and Web image searching is the most difficult compared to video or audio searches. Hence, online users need to put in more effort when searching multimedia contents, especially for image searches. Most interactions in Web image searching occur during query reformulation. While log analysis provides intriguing views on how the majority of users search, their search needs or motivations are ultimately neglected. User studies on image searching have attempted to understand users’ search contexts in terms of users’ background (e.g., knowledge, profession, motivation for search and task types) and the search outcomes (e.g., use of retrieved images, search performance). However, these studies typically focused on particular domains with a selective group of professional users. General users’ Web image searching contexts and behaviors are little understood although they represent the majority of online image searching activities nowadays. We argue that only by understanding Web image users’ contexts can the current Web search engines further improve their usefulness and provide more efficient searches. In order to understand users’ search contexts, a user study was conducted based on university students’ Web image searching in News, Travel, and commercial Product domains. The three search domains were deliberately chosen to reflect image users’ interests in people, time, event, location, and objects. We investigated participants’ Web image searching behavior, with the focus on query reformulation and search strategies. Participants’ search contexts such as their search background, motivation for search, and search outcomes were gathered by questionnaires. The searching activity was recorded with participants’ think aloud data for analyzing significant search patterns. The relationships between participants’ search contexts and corresponding search strategies were discovered by Grounded Theory approach. Our key findings include the following aspects: - Effects of users' interactive intents on query reformulation patterns and search strategies - Effects of task domain on task specificity and task difficulty, as well as on some specific searching behaviors - Effects of searching experience on result expansion strategies A contextual image searching model was constructed based on these findings. The model helped us understand Web image searching from user perspective, and introduced a context-aware searching paradigm for current retrieval systems. A query recommendation tool was also developed to demonstrate how users’ query reformulation contexts can potentially contribute to more efficient searching

Navigation, findability and the usage of cultural heritage on the web: an exploratory study

The present thesis investigates the usage of cultural heritage resources on the web. In recent years cultural heritage objects has been digitalized and made available on the web for the general public to use. The thesis addresses to what extent the digitalized material is used, and how findable it is on the web. On the web resources needs to be findable in order to be visited and used. The study is done at the intersection of several research areas in Library and Information Science; Information Seeking/Human Information Behaviour, Interactive Information Retrieval, and Webometrics. The two thesis research questions focus on different aspects of the study: (1) findability on the web; and (2) the usage and the users. The usage of the cultural heritage is analysed with Savolainen’s Everyday Life Information Seeking (ELIS) framework. The IS&R framework by Ingwersen and Järvelin is the main theoretical foundation, and a conceptual framework is developed so the examined aspects could be related to each other more clearly. An important distinction in the framework is between object and resource. An object is a single document, file or html page, whereas a resource is a collection of objects, e.g. a cultural heritage web site. Three webometric levels are used to both combine and distinguish the data types: usage, content, and structure. The interaction between the system and its users’ information search process was divided into query dependent and query independent aspects. The query dependent aspects contain the information need on the user side and the topic of the content on the system side. The query independent aspects are the structural findability on the system side and the users search skills on the user side. The conceptual framework is summarised in the User-Resource Interaction (URI) model. The research design is a methodological triangulation, in the form of a mixed methods approach in order to obtain measures and indicators of the resources and the usage from different angels. Four methods are used: site structure analysis; log analysis; web survey; and findability analysis. The research design is both sequential and parallel, the site structure analysis preceded the log analysis and the findability analysis, and the web survey was employed independent of the other methods. Three Danish resources are studied: Arkiv for Dansk Litteratur (ADL), a collection of literary texts written by authors; Kunst Index Danmark (KID), an index of the holdings in the Danish art museums; and Guaman Poma Inch Chronicle (Poma), a digitalized manuscript on the UNESCO list of World cultural heritage. The studied log covers all usage during the period October to December 2010. The site structure is analysed so the resources can be described as different levels, based on function and content. The results from the site structure analysis are used both in the log analysis and the findability analysis, as well as a way to describe the resources. In the log analysis navigation strategies and navigation patterns are studied. Navigation through a web search engine is the most common way to reach the resources, but both direct navigation and link navigation are also used in all three resources. Most users arrive in the middle level in ADL and KID, at information on authors and artists. On average cultural heritage objects are viewed in half of the session. In the analysis of the web survey answers two groups of users’ are distinguished, the professional user in a work context and users in a hobby or leisure context. School or study as a context is prominent in Guaman Poma, the Inca Chronicle. Generally are pages about the cultural heritage more frequently visited than the digitized cultural heritage objects. In the findability framework six aspects are identified as central for the findability of an object on the web: attributes of the object, accessibility, internal navigation, internal search, reachability and web prestige. The six aspects are evaluated through seven indicators. All studied objects are findable in the analysis using the findability framework. A findability issue in KID is the use of the secure https protocol instead of http, which leads to the objects in KID having no PageRank value in Google and thereby a lower ranking in comparison to similar objects with a PageRank value. The internal findability is reduced for the objects in top of all three resources, e.g. the first page, due to the focus of the internal search engine on the cultural heritage objects. Several possible adjustment or developments of the findability frameworks is discussed, such as changing the weightning between the aspects measured, alternative scores and automated measuring. In conclusion, the investigation adds to our knowledge about how resources with digitalized cultural heritage are accessed and used, as well as how findable they are. The thesis provides both theoretical and conceptual contributions to research. The IS&R framework has been adapted to the web, the information search process was split into query dependent and query independent aspects, and a whole findability framework has been developed. Both the empirical findings and the theoretical advancements support the development of better access to web resources

Navigation, findability and the usage of cultural heritage on the web:an exploratory study

The present thesis investigates the usage of cultural heritage resources on the web. In recent years cultural heritage objects has been digitalized and made available on the web for the general public to use. The thesis addresses to what extent the digitalized material is used, and how findable it is on the web. On the web resources needs to be findable in order to be visited and used. The study is done at the intersection of several research areas in Library and Information Science; Information Seeking/Human Information Behaviour, Interactive Information Retrieval, and Webometrics. The two thesis research questions focus on different aspects of the study: (1) findability on the web; and (2) the usage and the users. The usage of the cultural heritage is analysed with Savolainen’s Everyday Life Information Seeking (ELIS) framework. The IS&R framework by Ingwersen and Järvelin is the main theoretical foundation, and a conceptual framework is developed so the examined aspects could be related to each other more clearly. An important distinction in the framework is between object and resource. An object is a single document, file or html page, whereas a resource is a collection of objects, e.g. a cultural heritage web site. Three webometric levels are used to both combine and distinguish the data types: usage, content, and structure. The interaction between the system and its users’ information search process was divided into query dependent and query independent aspects. The query dependent aspects contain the information need on the user side and the topic of the content on the system side. The query independent aspects are the structural findability on the system side and the users search skills on the user side. The conceptual framework is summarised in the User-Resource Interaction (URI) model. The research design is a methodological triangulation, in the form of a mixed methods approach in order to obtain measures and indicators of the resources and the usage from different angels. Four methods are used: site structure analysis; log analysis; web survey; and findability analysis. The research design is both sequential and parallel, the site structure analysis preceded the log analysis and the findability analysis, and the web survey was employed independent of the other methods. Three Danish resources are studied: Arkiv for Dansk Litteratur (ADL), a collection of literary texts written by authors; Kunst Index Danmark (KID), an index of the holdings in the Danish art museums; and Guaman Poma Inch Chronicle (Poma), a digitalized manuscript on the UNESCO list of World cultural heritage. The studied log covers all usage during the period October to December 2010. The site structure is analysed so the resources can be described as different levels, based on function and content. The results from the site structure analysis are used both in the log analysis and the findability analysis, as well as a way to describe the resources. In the log analysis navigation strategies and navigation patterns are studied. Navigation through a web search engine is the most common way to reach the resources, but both direct navigation and link navigation are also used in all three resources. Most users arrive in the middle level in ADL and KID, at information on authors and artists. On average cultural heritage objects are viewed in half of the session. In the analysis of the web survey answers two groups of users’ are distinguished, the professional user in a work context and users in a hobby or leisure context. School or study as a context is prominent in Guaman Poma, the Inca Chronicle. Generally are pages about the cultural heritage more frequently visited than the digitized cultural heritage objects. In the findability framework six aspects are identified as central for the findability of an object on the web: attributes of the object, accessibility, internal navigation, internal search, reachability and web prestige. The six aspects are evaluated through seven indicators. All studied objects are findable in the analysis using the findability framework. A findability issue in KID is the use of the secure https protocol instead of http, which leads to the objects in KID having no PageRank value in Google and thereby a lower ranking in comparison to similar objects with a PageRank value. The internal findability is reduced for the objects in top of all three resources, e.g. the first page, due to the focus of the internal search engine on the cultural heritage objects. Several possible adjustment or developments of the findability frameworks is discussed, such as changing the weightning between the aspects measured, alternative scores and automated measuring. In conclusion, the investigation adds to our knowledge about how resources with digitalized cultural heritage are accessed and used, as well as how findable they are. The thesis provides both theoretical and conceptual contributions to research. The IS&R framework has been adapted to the web, the information search process was split into query dependent and query independent aspects, and a whole findability framework has been developed. Both the empirical findings and the theoretical advancements support the development of better access to web resources

Investigation on Applying Modular Ontology to Statistical Language Model for Information Retrieval

The objective of this research is to provide a novel approach to improving retrieval performance by exploiting Ontology with the statistical language model (SLM). The proposed methods consist of two major processes, namely ontology-based query expansion (OQE) and ontology-based document classification (ODC). Research experiments have required development of an independent search tool that can combine the OQE and ODC in a traditional SLM-based information retrieval (IR) process using a Web document collection. This research considers the ongoing challenges of modular ontology enhanced SLM-based search and addresses three contribution aspects. The first concerns how to apply modular ontology to query expansion, in a bespoke language model search tool (LMST). The second considers how to incorporate OQE with the language model to improve the search performance. The third examines how to manipulate such semantic-based document classification to improve the smoothing accuracy. The role of ontology in the research is to provide formally described domains of interest that serve as context, to enhance system query effectiveness

Page Ranking Systems: Axiomatisation and Experimentation

Ranking a set of objects based on the relationships between them is fundamental for use with search engines, e-commerce websites and in the field of bibliometrics. Two of the most prominent search ranking algorithms are PageRank and SALSA (Stochastic Approach to Link-Structure Analysis). In this thesis, we further explore the connections between page ranking algorithms and the theory of social choice, providing a basis for theoretical assessment of a weighted version of PageRank and we create and assess a new page ranking al- gorithm, combining ideas from both PageRank and SALSA which we call Query- Independent SALSA. We justify the use of weighted PageRank from a theoretical perspective by providing a set of axioms which characterize the algorithm. We provide a tighter bound for our derivation than that of Altman et al and show that each of our axioms are independent. We describe a query-independent version of SALSA, using ideas from the PageRank algorithm and test this on a real-world subgraph of the web graph. We find that our new algorithm, Query-Independent Stochastic Approach to Link-Structure Analysis (QISALSA) slightly outperforms PageRank on two measures and under-performs on one measure. We suggest that the approach of combining aspects of both algo-rithms may be less eective than precomputational methods for query-dependent algorithms

Learning to Diversify Web Search Results with a Document Repulsion Model

Search diversification (also called diversity search), is an important approach to tackling the query ambiguity problem in information retrieval. It aims to diversify the search results that are originally ranked according to their probabilities of relevance to a given query, by re-ranking them to cover as many as possible different aspects (or subtopics) of the query. Most existing diversity search models heuristically balance the relevance ranking and the diversity ranking, yet lacking an efficient learning mechanism to reach an optimized parameter setting. To address this problem, we propose a learning-to-diversify approach which can directly optimize the search diversification performance (in term of any effectiveness metric). We first extend the ranking function of a widely used learning-to-rank framework, i.e., LambdaMART, so that the extended ranking function can correlate relevance and diversity indicators. Furthermore, we develop an effective learning algorithm, namely Document Repulsion Model (DRM), to train the ranking function based on a Document Repulsion Theory (DRT). DRT assumes that two result documents covering similar query aspects (i.e., subtopics) should be mutually repulsive, for the purpose of search diversification. Accordingly, the proposed DRM exerts a repulsion force between each pair of similar documents in the learning process, and includes the diversity effectiveness metric to be optimized as part of the loss function. Although there have been existing learning based diversity search methods, they often involve an iterative sequential selection process in the ranking process, which is computationally complex and time consuming for training, while our proposed learning strategy can largely reduce the time cost. Extensive experiments are conducted on the TREC diversity track data (2009, 2010 and 2011). The results demonstrate that our model significantly outperforms a number of baselines in terms of effectiveness and robustness. Further, an efficiency analysis shows that the proposed DRM has a lower computational complexity than the state of the art learning-to-diversify methods

Diversification Based Static Index Pruning - Application to Temporal Collections

Nowadays, web archives preserve the history of large portions of the web. As medias are shifting from printed to digital editions, accessing these huge information sources is drawing increasingly more attention from national and international institutions, as well as from the research community. These collections are intrinsically big, leading to index files that do not fit into the memory and an increase query response time. Decreasing the index size is a direct way to decrease this query response time. Static index pruning methods reduce the size of indexes by removing a part of the postings. In the context of web archives, it is necessary to remove postings while preserving the temporal diversity of the archive. None of the existing pruning approaches take (temporal) diversification into account. In this paper, we propose a diversification-based static index pruning method. It differs from the existing pruning approaches by integrating diversification within the pruning context. We aim at pruning the index while preserving retrieval effectiveness and diversity by pruning while maximizing a given IR evaluation metric like DCG. We show how to apply this approach in the context of web archives. Finally, we show on two collections that search effectiveness in temporal collections after pruning can be improved using our approach rather than diversity oblivious approaches