Using bag-of-concepts to improve the performance of support vector machines in text categorization

This paper investigates the use of concept-based representations for text categorization. We introduce a new approach to create concept-based text representations, and apply it to a standard text categorization collection. The representations are used as input to a Support Vector Machine classifier, and the results show that there are certain categories for which concept-based representations constitute a viable supplement to word-based ones. We also demonstrate how the performance of the Support Vector Machine can be improved by combining representations

Paper Prototyping a Social Mobile Service

Methods for design and evaluation of interactive applications are not readily applicable to mobile services. By modifying an existing paper prototyping method we evaluated a mobile social service for providing user-based tips in a shopping mall. The evaluation showed that tips can be pushed to users and that they can accept that a complex user interface is presented on a small screen. Although the evaluation took place in an office environment, we received feedback on functionality of the service in the context of the shopping mall. Our evaluation indicates that simple prototyping techniques can be used for informative evaluations of mobile services that are heavily context dependent

GeoNotes: A Location-based Information System for Public Spaces

The basic idea behind location-based information systems is to connect information pieces to positions in outdoor or indoor space. Through position technologies such as Global Positioning System (GPS), GSM positioning, Wireless LAN positioning o

Consensus and opinions; quality and churn

The role of the web user is under transformation from merely being an information consumer to also being a content provider, ``from information age to participation age'', in the words of Sun CEO Scott McNealy. This increase in participation is most obviously manifested by the growth of online communities, weblogs (blogs), and various forms of cooperative and participatory publication of information. One main factor in the shift towards participation is the advent of authoring tools for wikipedias and blogs. Such tools have decreased the threshold for publishing material online considerably --- it is no longer necessary to have knowledge about the technical workings of the web to be able to use it for making information available to a massive number of potential readers. (Although the lion's share of information produced will probably remain in text form in the foreseeable future, it should be noted that other modalities, such as podcasts, screencasts, films and images, are increasingly attracting interest.) The dynamic nature of blogs and wikipedias poses new challenges to the field of information access and refinement; new theories, methods, and tools for alleviating the burden of digesting information on behalf of the readers are clearly needed. This paper presents some issues on readership and participation we are currently considering

Learning and scalability in personalized information retrieval and filtering

This licentiate thesis is composed of three papers on personalized information retrieval and filtering. The first paper deals with personalized information retrieval. A method is presented that learns from user feedback in a long-term fashion. An experimental evaluation of the method on a standard test collection demonstrates highly improved precision and recall for novel user queries. In the second paper, a technique for large-scale personalized collaborative filtering is developed. The technique is based on inverted files, similar to algorithms used in text retrieval. Experimental results show that the inverted file search strategy is many times faster than the in-memory base line method. Further improvements are also demonstrated by the use of early termination heuristics. The third paper describes the architecture and implementation of a system for personalized information filtering. The filtering functionality is general, and may be used for several tasks such as text classification and collaborative filtering. The system may be used in a stand-alone application, or in a client-server environment. Early versions of the system have already been used successfully in a number of research projects

Algorithms and Representations for Personalised Information Access

Personalised information access systems use historical feedback data, such as implicit and explicit ratings for textual documents and other items, to better locate the right or relevant information for individual users. Three topics in personalised information access are addressed: learning from relevance feedback and document categorisation by the use of concept-based text representations, the need for scalable and accurate algorithms for collaborative filtering, and the integration of textual and collaborative information access. Two concept-based representations are investigated that both map a sparse high-dimensional term space to a dense concept space. For learning from relevance feedback, it is found that the representation combined with the proposed learning algorithm can improve the results of novel queries, when queries are more elaborate than a few terms. For document categorisation, the representation is found useful as a complement to a traditional word-based one. For collaborative filtering, two algorithms are proposed: the first for the case where there are a large number of users and items, and the second for use in a mobile device. It is demonstrated that memory-based collaborative filtering can be more efficiently implemented using inverted files, with equal or better accuracy, and that there is little reason to use the traditional in-memory vector approach when the data is sparse. An empirical evaluation of the algorithm for collaborative filtering on mobile devices show that it can generate accurate predictions at a high speed using a small amount of resources. For integration, a system architecture is proposed where various combinations of content-based and collaborative filtering can be implemented. The architecture is general in the sense that it provides an abstract representation of documents and user profiles, and provides a mechanism for incorporating new retrieval and filtering algorithms at any time. In conclusion this thesis demonstrates that information access systems can be personalised using scalable and accurate algorithms and representations for the increased benefit of the user

Inverted file search algorithms for collaborative filtering

This paper explores the possibility of using a disk based inverted file structure for collaborative filtering. Our hypothesis is that this allows for faster calculation of predictions and also that early termination heuristics may be used to further speed up the filtering process and perhaps even improve the quality of the predictions. In an experiment on the EachMovie dataset this was tested. Our results indicate that searching the inverted file structure is many times faster than general in-memory vector search, even for very large profiles. The Continue termination heuristics produces the best ranked predictions in our experiments, and Quit is the top performer in terms of speed

Incremental Collaborative Filtering for Mobile Devices

This paper describes how collaborative filtering can be used for mobile devices. When the user is connected to a central repository, the algorithm selects a subset of profiles to store on the device. When the user is not connected to the repository, the predictions can be incrementally updated to reflect new or updated ratings. Experiments on a movie data set show that the method can dramatically reduce the data needed while still performing nearly as good as a centralized approach