User modeling

About: User modeling is a research topic. Over the lifetime, 10701 publications have been published within this topic receiving 278012 citations.

Adaptive news access

Abstract: This chapter describes how the adaptive web technologies discussed in this book have been applied to news access. First, we provide an overview of different types of adaptivity in the context of news access and identify corresponding algorithms. For each adaptivity type, we briefly discuss representative systems that use the described techniques. Next, we discuss an in-depth case study of a personalized news system. As part of this study, we outline a user modeling approach specifically designed for news personalization, and present results from an evaluation that attempts to quantify the effect of adaptive news access from a user perspective. We conclude by discussing recent trends and novel systems in the adaptive news space.

Exploring Versus Exploiting when Learning User Models for Text Recommendation

Abstract: The text recommendation task involves delivering sets of documents to users on the basis of user models. These models are improved over time, given feedback on the delivered documents. When selecting documents to recommend, a system faces an instance of the exploration/exploitation tradeoff: whether to deliver documents about which there is little certainty, or those which are known to match the user model learned so far. In this paper, a simulation is constructed to investigate the effects of this tradeoff on the rate of learning user models, and the resulting compositions of the sets of recommended documents, in particular World-Wide Web pages. Document selection strategies are developed which correspond to different points along the tradeoff. Using an exploitative strategy, our results show that simple preference functions can successfully be learned using a vector-space representation of a user model in conjunction with a gradient descent algorithm, but that increasingly complex preference functions lead to a slowing down of the learning process. Exploratory strategies are shown to increase the rate of user model acquisition at the expense of presenting users with suboptimal recommendations; in addition they adapt to user preference changes more rapidly than exploitative strategies. These simulated tests suggest an implementation for a simple control that is exposed to users, allowing them to vary a system‘s document selection behavior depending on individual circumstances.

An adaptive system for the personalized access to news

Abstract: Personalization is one of the keys for the success of web services. In this paper we present SeAN (Server for Adaptive News), an adaptive system for the personalized access to news servers on the WWW. The aims of the system are (i) to select the sections (topics) and news in the server that are most relevant for each user, (ii) to customize the detail level of each news item to the user's characteristics and (iii) to select the advertisements that are most appropriate for each page and user. In the paper we discuss the functionalities of the system and we present the choices we made in its design. In particular, we focus on the techniques we adopted for structuring the news archive, for creating and maintaining the user model and for generating the personalized hypertext for browsing the news server.