Probabilistic learning for selective dissemination of information

New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile

A model for mobile content filtering on non-interactive recommendation systems

To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems

Music information retrieval: conceptuel framework, annotation and user behaviour

Understanding music is a process both based on and influenced by the knowledge and experience of the listener. Although content-based music retrieval has been given increasing attention in recent years, much of the research still focuses on bottom-up retrieval techniques. In order to make a music information retrieval system appealing and useful to the user, more effort should be spent on constructing systems that both operate directly on the encoding of the physical energy of music and are flexible with respect to users’ experiences. This thesis is based on a user-centred approach, taking into account the mutual relationship between music as an acoustic phenomenon and as an expressive phenomenon. The issues it addresses are: the lack of a conceptual framework, the shortage of annotated musical audio databases, the lack of understanding of the behaviour of system users and shortage of user-dependent knowledge with respect to high-level features of music. In the theoretical part of this thesis, a conceptual framework for content-based music information retrieval is defined. The proposed conceptual framework - the first of its kind - is conceived as a coordinating structure between the automatic description of low-level music content, and the description of high-level content by the system users. A general framework for the manual annotation of musical audio is outlined as well. A new methodology for the manual annotation of musical audio is introduced and tested in case studies. The results from these studies show that manually annotated music files can be of great help in the development of accurate analysis tools for music information retrieval. Empirical investigation is the foundation on which the aforementioned theoretical framework is built. Two elaborate studies involving different experimental issues are presented. In the first study, elements of signification related to spontaneous user behaviour are clarified. In the second study, a global profile of music information retrieval system users is given and their description of high-level content is discussed. This study has uncovered relationships between the users’ demographical background and their perception of expressive and structural features of music. Such a multi-level approach is exceptional as it included a large sample of the population of real users of interactive music systems. Tests have shown that the findings of this study are representative of the targeted population. Finally, the multi-purpose material provided by the theoretical background and the results from empirical investigations are put into practice in three music information retrieval applications: a prototype of a user interface based on a taxonomy, an annotated database of experimental findings and a prototype semantic user recommender system. Results are presented and discussed for all methods used. They show that, if reliably generated, the use of knowledge on users can significantly improve the quality of music content analysis. This thesis demonstrates that an informed knowledge of human approaches to music information retrieval provides valuable insights, which may be of particular assistance in the development of user-friendly, content-based access to digital music collections

Is query translation a distinct task from search?

INTRODUCTION The University of Sheffield participated in iCLEF 2002 using, as a test-bed, the prototype under development in the Clarity project. Clarity is an EU funded project aimed at developing a system for cross-language information retrieval for so-called low density languages, those with few translation resources. Currently translation between English and Finnish is supported; soon Swedish will be added and in the near future Latvian and Lithuanian. Clarity is being developed in a user-centred way with user involvement from the beginning. The design of the first user interface was based on current best practise, particular attention was paid to empirical evidence for a specific design choice. Six paper-based interface mock-ups representing important points in the cross-language search task were generated and presented for user assessment as a part of an extensive user study. The study (reported in Petrelli et al. 2002) was conducted to understand users and uses of cross-language information retrieval systems. Many different techniques were applied: contextual enquiry, interviews, questionnaires, informal evaluation of existing cross-language technology, and participatory design sessions with the interface mock-ups mentioned above. As a result, a user class profile was sketched and a long list of user requirements was compiled. As a followup, a redesign session took place and the new system was designed for users whoknow the language(s) they are searching (polyglots); • search for writing (journalists, translators business analysts); • have limited searching skills; • know the topic in advance or will learn/read on it while searching; • use many languages in the same search session and often swap between them. New system features were listed as important and the user interface was redesigned. Considering the result of the study the new interface allowed the user to dynamically change the language setting from query to query, hid the query translation and showed the retrieved set as ranked list primary. Despite the fact that this new design was considered to be more effective, a comparison between the first layout based on the relevant literature and the new one based on the user study was considered an important research question. In particular, the choice of hiding the query translation was considered an important design decision, against the common agreement to allow and support the user in controlling the system actions. Thus the participation of Sheffield in iCLEF was organized around the idea of checking if the user should validate the query translation before the search is run or instead if the system should perform the translation and search in a single step without any user’s supervision

Personalised video retrieval: application of implicit feedback and semantic user profiles

A challenging problem in the user profiling domain is to create profiles of users of retrieval systems. This problem even exacerbates in the multimedia domain. Due to the Semantic Gap, the difference between low-level data representation of videos and the higher concepts users associate with videos, it is not trivial to understand the content of multimedia documents and to find other documents that the users might be interested in. A promising approach to ease this problem is to set multimedia documents into their semantic contexts. The semantic context can lead to a better understanding of the personal interests. Knowing the context of a video is useful for recommending users videos that match their information need. By exploiting these contexts, videos can also be linked to other, contextually related videos. From a user profiling point of view, these links can be of high value to recommend semantically related videos, hence creating a semantic-based user profile. This thesis introduces a semantic user profiling approach for news video retrieval, which exploits a generic ontology to put news stories into its context. Major challenges which inhibit the creation of such semantic user profiles are the identification of user's long-term interests and the adaptation of retrieval results based on these personal interests. Most personalisation services rely on users explicitly specifying preferences, a common approach in the text retrieval domain. By giving explicit feedback, users are forced to update their need, which can be problematic when their information need is vague. Furthermore, users tend not to provide enough feedback on which to base an adaptive retrieval algorithm. Deviating from the method of explicitly asking the user to rate the relevance of retrieval results, the use of implicit feedback techniques helps by learning user interests unobtrusively. The main advantage is that users are relieved from providing feedback. A disadvantage is that information gathered using implicit techniques is less accurate than information based on explicit feedback. In this thesis, we focus on three main research questions. First of all, we study whether implicit relevance feedback, which is provided while interacting with a video retrieval system, can be employed to bridge the Semantic Gap. We therefore first identify implicit indicators of relevance by analysing representative video retrieval interfaces. Studying whether these indicators can be exploited as implicit feedback within short retrieval sessions, we recommend video documents based on implicit actions performed by a community of users. Secondly, implicit relevance feedback is studied as potential source to build user profiles and hence to identify users' long-term interests in specific topics. This includes studying the identification of different aspects of interests and storing these interests in dynamic user profiles. Finally, we study how this feedback can be exploited to adapt retrieval results or to recommend related videos that match the users' interests. We analyse our research questions by performing both simulation-based and user-centred evaluation studies. The results suggest that implicit relevance feedback can be employed in the video domain and that semantic-based user profiles have the potential to improve video exploration

Using concept similarity in cross ontology for adaptive e-Learning systems

Abstracte-Learning is one of the most preferred media of learning by the learners. The learners search the web to gather knowledge about a particular topic from the information in the repositories. Retrieval of relevant materials from a domain can be easily implemented if the information is organized and related in some way. Ontologies are a key concept that helps us to relate information for providing the more relevant lessons to the learner. This paper proposes an adaptive e-Learning system, which generates a user specific e-Learning content by comparing the concepts with more than one system using similarity measures. A cross ontology measure is defined, which consists of fuzzy domain ontology as the primary ontology and the domain expert’s ontology as the secondary ontology, for the comparison process. A personalized document is provided to the user with a user profile, which includes the data obtained from the processing of the proposed method under a User score, which is obtained through the user evaluation. The results of the proposed e-Learning system under the designed cross ontology similarity measure show a significant increase in performance and accuracy under different conditions. The assessment of the comparative analysis, showed the difference in performance of our proposed method over other methods. Based on the assessment results it is proved that the proposed approach is effective over other methods

AVIR – Audio-Visual Indexing and Retrieval for Non IT Expert Users

The AVIR proposal originates from the demand for new solutions allowing common users to easily access, store and retrieve relevant audio-visual information from the vast amounts of resources at their disposal. The next generation of television systems will be connected to many sources of information and entertainment (TV-and radio from air, cable or satellite, video and audio libraries, video tape/disk recorders, Internet). Literally hundreds of channels will soon be offered to the user, which could be disoriented by this overload of information. Users will not pay for just more extra channels, but will appreciate if the content in the channels is easily accessible and, more importantly, can be easily selected according to the user's personal interest. This can only be achieved if the broadcaster delivers meta-data describing the actual content in sufficient detail enabling for automatic handling by agents residing on the end user's system. AVIR investigates on novel procedures for automatic analysis and indexing of audio-visual information, specifically meant to support consumer services. The objective of this project is to investigate and experiment end-to-end solutions for delivering new added value services on top of digital video broadcast services, which will enable a better exploitation of multimedia information resources by non-IT experts. As a result the project is building a prototype service user platform and will demonstrate its feasibility on a broadcast delivery chain. It takes into account extraction of high quality meta-data and electronic delivery of meta-data associated to audio-visual content, including adaptation of consumer receivers and recorders towards a personalized multimedia repository. Intelligent agents based on a user interest profile will help the user to browse and access most relevant programmes via an intelligent, personal electronic guide. A low cost, high capacity home storage device, will also be used to increment the capabilities of the consumer system. Thanks to the received descriptors, advanced retrieval features can be implemented on the stored assets and, in combination with the user’s profile, automatic recording feature is possible. A visual navigation system, a search engine and agents will help the user identifyvideo material of interest on the home video-recorder, transfo rming it into a personal multimedia repository