Enhanced information retrieval using domain-specific recommender models

The objective of an information retrieval (IR) system is to retrieve relevant items which meet a user information need. There is currently significant interest in personalized IR which seeks to improve IR effectiveness by incorporating a model of the user’s interests. However, in some situations there may be no opportunity to learn about the interests of a specific user on a certain topic. In our work, we propose an IR approach which combines a recommender algorithm with IR methods to improve retrieval for domains where the system has no opportunity to learn prior information about the user’s knowledge of a domain for which they have not previously entered a query. We use search data from other previous users interested in the same topic to build a recommender model for this topic. When a user enters a query on a topic, new to this user, an appropriate recommender model is selected and used to predict a ranking which the user may find interesting based on the behaviour of previous users with similar queries. The recommender output is integrated with a standard IR method in a weighted linear combination to provide a final result for the user. Experiments using the INEX 2009 data collection with a simulated recommender training set show that our approach can improve on a baseline IR system

Enhancing Information Retrieval Relevance Using Touch Dynamics on Search Engine

Using Touch Dynamics on Search Engine is an attempt to establish the possibilities of using user touch behavior which is monitored and several unique features are extracted. The unique features are used for identifying users and their traits according to the touch dynamics. The results can be used for defining automatic user unique searching behavior. Touch dynamics has been discussed in several studies in the context of user authentication and biometric identification for security purposes. This study establishes the possibility of integrating touch dynamics results for identifying user searching preferences and interests. This study investigates a technique of combining personalized search with touch dynamics results information as an approach for determining user preferences, interest measurement and context. Keywords: Personalized Search, Information Retrieval, Touch Dynamics, Search Engin

SocialDining: Design and Analysis of a Group Recommendation Application in a Mobile Context

Mobile social networks are rapidly becoming an important new domain showcasing the power of mobile computing systems. These networks combine mobile location information with social networking data to enable fully context-aware environments. This paper describes SocialDining, a system that fuses mobile and social data to power novel context-aware recommendation services that provide recommendations to small groups of users who want to meet together for food or drink at local restaurants. We report our analysis on the data collected from 31 users for the SocialDining application over the course of 15 weeks

MINING AND VERIFICATION OF TEMPORAL EVENTS WITH APPLICATIONS IN COMPUTER MICRO-ARCHITECTURE RESEARCH

Computer simulation programs are essential tools for scientists and engineers to understand a particular system of interest. As expected, the complexity of the software increases with the depth of the model used. In addition to the exigent demands of software engineering, verification of simulation programs is especially challenging because the models represented are complex and ridden with unknowns that will be discovered by developers in an iterative process. To manage such complexity, advanced verification techniques for continually matching the intended model to the implemented model are necessary. Therefore, the main goal of this research work is to design a useful verification and validation framework that is able to identify model representation errors and is applicable to generic simulators. The framework that was developed and implemented consists of two parts. The first part is First-Order Logic Constraint Specification Language (FOLCSL) that enables users to specify the invariants of a model under consideration. From the first-order logic specification, the FOLCSL translator automatically synthesizes a verification program that reads the event trace generated by a simulator and signals whether all invariants are respected. The second part consists of mining the temporal flow of events using a newly developed representation called State Flow Temporal Analysis Graph (SFTAG). While the first part seeks an assurance of implementation correctness by checking that the model invariants hold, the second part derives an extended model of the implementation and hence enables a deeper understanding of what was implemented. The main application studied in this work is the validation of the timing behavior of micro-architecture simulators. The study includes SFTAGs generated for a wide set of benchmark programs and their analysis using several artificial intelligence algorithms. This work improves the computer architecture research and verification processes as shown by the case studies and experiments that have been conducted

Enhanced web-based summary generation for search.

After a user types in a search query on a major search engine, they are presented with a number of search results. Each search result is made up of a title, brief text summary and a URL. It is then the user\u27s job to select documents for further review. Our research aims to improve the accuracy of users selecting relevant documents by improving the way these web pages are summarized. Improvements in accuracy will lead to time improvements and user experience improvements. We propose ReClose, a system for generating web document summaries. ReClose generates summary content through combining summarization techniques from query-biased and query-independent summary generation. Query-biased summaries generally provide query terms in context. Query-independent summaries focus on summarizing documents as a whole. Combining these summary techniques led to a 10% improvement in user decision making over Google generated summaries. Color-coded ReClose summaries provide keyword usage depth at a glance and also alert users to topic departures. Color-coding further enhanced ReClose results and led to a 20% improvement in user decision making over Google generated summaries. Many online documents include structure and multimedia of various forms such as tables, lists, forms and images. We propose to include this structure in web page summaries. We found that the expert user was insignificantly slowed in decision making while the majority of average users made decisions more quickly using summaries including structure without any decrease in decision accuracy. We additionally extended ReClose for use in summarizing large numbers of tweets in tracking flu outbreaks in social media. The resulting summaries have variable length and are effective at summarizing flu related trends. Users of the system obtained an accuracy of 0.86 labeling multi-tweet summaries. This showed that the basis of ReClose is effective outside of web documents and that variable length summaries can be more effective than fixed length. Overall the ReClose system provides unique summaries that contain more informative content than current search engines produce, highlight the results in a more meaningful way, and add structure when meaningful. The applications of ReClose extend far beyond search and have been demonstrated in summarizing pools of tweets

A Multi-Dimensional Approach for Framing Crowdsourcing Archetypes

All different kinds of organizations – business, public, and non-governmental alike – are becoming aware of a soaring complexity in problem solving, decision making and idea development. In a multitude of circumstances, multidisciplinary teams, high-caliber skilled resources and world-class computer suites do not suffice to cope with such a complexity: in fact, a further need concerns the sharing and ‘externalization’ of tacit knowledge already existing in the society. In this direction, participatory tendencies flourishing in the interconnected society in which we live today lead ‘collective intelligence’ to emerge as key ingredient of distributed problem solving systems going well beyond the traditional boundaries of organizations. Resulting outputs can remarkably enrich decision processes and creative processes carried out by indoor experts, allowing organizations to reap benefits in terms of opportunity, time and cost. Taking stock of the mare magnum of promising opportunities to be tapped, of the inherent diversity lying among them, and of the enormous success of some initiative launched hitherto, the thesis aspires to provide a sound basis for the clear comprehension and systematic exploitation of crowdsourcing. After a thorough literature review, the thesis explores new ways for formalizing crowdsourcing models with the aim of distilling a brand-new multi-dimensional framework to categorize various crowdsourcing archetypes. To say it in a nutshell, the proposed framework combines two dimensions (i.e., motivations to participate and organization of external solvers) in order to portray six archetypes. Among the numerous significant elements of novelty brought by this framework, the prominent one is the ‘holistic’ approach that combines both profit and non-profit, trying to put private and public sectors under a common roof in order to examine in a whole corpus the multi-faceted mechanisms for mobilizing and harnessing competence and expertise which are distributed among the crowd. Looking at how the crowd may be turned into value to be internalized by organizations, the thesis examines crowdsourcing practices in the public as well in the private sector. Regarding the former, the investigation leverages the experience into the PADGETS project through action research – drawing on theoretical studies as well as on intensive fieldwork activities – to systematize how crowdsourcing can be fruitfully incorporated into the policy lifecycle. Concerning the private realm, a cohort of real cases in the limelight is examined – having recourse to case study methodology – to formalize different ways through which crowdsourcing becomes a business model game-changer. Finally, the two perspectives (i.e., public and private) are coalesced into an integrated view acting as a backdrop for proposing next-generation governance model massively hinged on crowdsourcing. In fact, drawing on archetypes schematized, the thesis depicts a potential paradigm that government may embrace in the coming future to tap the potential of collective intelligence, thus maximizing the utilization of a resource that today seems certainly underexploited