Analysing web search logs to determine session boundaries for user-oriented learning

Incremental learning approaches based on user search activities provide a means of building adaptive information retrieval systems. To develop more effective user-oriented learning techniques for the Web, we need to be able to identify a meaningful session unit from which we can learn. Without this, we run a high risk of grouping together activities that are unrelated or perhaps not from the same user. We are interested in detecting boundaries of sequences between related activities (sessions) that would group the activities for a learning purpose. Session boundaries, in Reuters transaction logs, were detected automatically. The generated boundaries were compared with human judgements. The comparison confirmed that a meaningful session threshold for establishing these session boundaries was confined to a 11-15 minute range

User context and personalisation

The importance of user context as a means of delivering personalised and context-sensitive systems is discussed. Relevant aspects of personalisation and context technology are covered. The intention is to inspire those interested in Case-base reasoning and personalisation from background and experience in other disciplines such as information retrieval, adaptive user interfaces, user modelling and mobile computing. Descriptions of personalisation and context are followed by their use in information retrieval and their importance and use in ambient computing. Relevant literature that may be a motivating source for interested readers are provided. Various questions are also raised in initiating discussion on this topic

Capturing information need by learning user context

Learning techniques can be applied to help information retrieval systems adapt to users' specific needs. They can be used to learn from user searches to improve subsequent searches. This paper describes the approach taken to learn about particular users' contexts in order to improve document ranking produced by a probabilistic information retrieval system. The approach is based on the argument that there is a pattern in user queries in that they tend to remain within a particular context over online sessions. This context, if approximated, can improve system performance. While it is not uncommon to link the evidence from one query to the next within a particular online session, the approach here groups the evidence over different sessions. The paper concentrates on the user-oriented evaluation method used in order to determine whether or not the approach improved information retrieval system performance

AmbieSense: a system and reference architecture for personalised and context-sensitive information services for mobile users

The purpose of AmbieSense is to provide personalised, context-sensitive information to the mobile user. It is about augmenting digital information to physical objects, rooms, and areas. The aim is to provide relevant information to the right user and situation. Digital content is distributed from the surroundings and onto your mobile phone. An ambient information environment is provided by a combination of context tag technology, a software platform to manage and deliver the information, and personal computing devices to which the information is served. This paper describes how the AmbieSense reference architecture has been defined and used in order to deliver information to the mobile citizen at the right time, place and situation. Information is provided via specialist content providers. The application area addresses the information needs of travellers and tourists

Mining Newsorthy Topics from Social Media

Newsworthy stories are increasingly being shared through social networking platforms such as Twitter and Reddit, and journal-ists now use them to rapidly discover stories and eye-witness accounts. We present a technique that detects “bursts” of phrases on Twitter that is designed for a real-time topic-detection system. We describe a time-dependent variant of the classic tf-idf approach and group together bursty phrases that often appear in the same messages in order to identify emerging topics. We demonstrate our methods by analysing tweets corresponding to events drawn from the worlds of politics and sport. We created a user-centred “ground truth” to evaluate our methods, based on mainstream media accounts of the events. This helps ensure our methods remain practical. We compare several clustering and topic ranking methods to discover the characteristics of news-related collections, and show tha t different strategies are needed to detect emerging topics within them. We show that our methods successfully detect a range of different topics for each event and can retrieve messages (for example, tweets) that represent each topic for the user

Social Tagging: Exploring the Image, the Tags, and the Game

An increasing amount of images are being uploaded, shared, and retrieved on the Web. These large image collections need to be properly stored, organized and easily retrieved. Tags have a key role in image retrieval but it is difficult for those who upload the images to also undertake the quality tag assignment for potential future retrieval by others. Relying on professional keyword assignment is not a practical option for large image collections due to resource constraints. Although a number of content-based image retrieval systems have been launched, they have not demonstrated sufficient utility on large-scale image sources on the web, and are usually used as a supplement to existing text-based image retrieval systems. An alternative to professional image indexing can be social tagging -- with two major types being photo-sharing networks and image labeling games. Here we analyze these applications to evaluate their usefulness from the semantic point of view. We also investigate whether social tagging behaviour can be managed. The findings of the study have shown that social tagging can generate a sizeable number of tags that can be classified as interpretive for an image, and that tagging behaviour has a manageable and adjustable nature depending on tagging guidelines

Content retrieval and mobile users: An outdoor investigation of an ambient travel guide

People's information needs change as they encounter new situations. The need for an ambient information environment becomes more evident in the case of the mobile traveller where situated information access is one of the main challenges. The motivation for this work has been to provide relevant information to the right situation and user in an ambient manner. Our way to solve this is to deliver personalised and context-aware information to the mobile user. To this end we have developed a platform, and prototype applications for travellers, and tourists. The system integrates our own tag technology with information from content service providers covering both general travel guide and local information. The development methodology is user-centred, iterative, and progressive in nature. It combines information retrieval (IR) test and evaluation techniques with iterative and user-centred development techniques at the test and evaluation phase. Combining the two disciplines gives us the ability to test and evaluate both the information aspects and the interaction aspects of any information system in parallel. Another advantage would be that one can develop content and software in parallel. This paper focuses on the IR test and evaluation framework that has been used in conjunction with the user-centred development. We emphasize the importance of performing IR test and evaluation for mobile systems in terms of users’ situations and tasks. The paper presents the results of some of the findings from a preliminary user test in an outdoor scenario. The test took place in a popular tourist destination in Spain

An investigation into the application of machine learning in information retrieval

There is an increasing variety of online databases available which are also evergrowing in size. In retrieving information from these sources, it is important not only to have effective and efficient retrieval techniques but also to enable some form of adaptation to users’ specific needs. Frequent users, in particular, should be able to benefit from their high use of the information retrieval system. A machine learning approach can be applied to help the system adapt to users’ specific needs. It is argued that users have a particular context within which their queries are formed. It is likely that consecutive queries for a particular user will be related in that they will be part of the same context. Thus, a context learner is proposed. In this investigation, the context learner is used for enhancing document ordering in partial match systems