User modeling

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

Learning implicit user interest hierarchy for context in personalization

Abstract: To provide a more robust context for personalization, we desire to extract a continuum of general (long-term) to specific (short-term) interests of a user. Our proposed approach is to learn a user interest hierarchy (UIH) from a set of web pages visited by a user. We devise a divisive hierarchical clustering (DHC) algorithm to group words (topics) into a hierarchy where more general interests are represented by a larger set of words. Each web page can then be assigned to nodes in the hierarchy for further processing in learning and predicting interests. This approach is analogous to building a subject taxonomy for a library catalog system and assigning books to the taxonomy. Our approach does not need user involvement and learns the UIH "implicitly." Furthermore, it allows the original objects, web pages, to be assigned to multiple topics (nodes in the hierarchy). In this paper, we focus on learning the UIH from a set of visited pages. We propose a few similarity functions and dynamic threshold-finding methods, and evaluate the resulting hierarchies according to their meaningfulness and shape

Adaptive user interfaces

Abstract: Systems, methods, and computer-readable storage media for creating and displaying adaptive user interfaces are disclosed. An example method includes receiving a user interface by an application development environment, the application development environment providing the ability to allow authoring of a user interface that adapts to a screen size with any first abstracted size class value and any second abstracted size class value. The method then includes creating an application including the user interface wherein the application is configured to: determine a screen size of a device, the screen size including a first abstracted size class value and a second abstracted size class value; adapt the user interface according to the screen size including the first abstracted size class value and the second abstracted size class value; and display the adapted user interface on the device.

System and method for context based query augmentation

Abstract: A system and method for context based query augmentation. A question is received over a network from a questioning user comprising an identification of a user and at least one question criteria. A first query is formulated so as to search, via the network, for user profile data, social network data, spatial data, temporal data and topical data so as to identify user context data relevant to question criteria. The question is modified using the user context data to create at least one modified question having at least one additional criteria based on the user context data. A second query is formulated so as to search, via the network, for knowledge data, user profile data, social network data, spatial data, temporal data and topical data so as to identify knowledge data relevant to the identified user and the modified question criteria. The knowledge data is transmitted, over the network, to the questioning user.

Scalable distributed inference of dynamic user interests for behavioral targeting

Abstract: Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current.We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.

Living Lab Research Landscape: From User Centred Design and User Experience towards User Cocreation

Abstract: New paradigms, such as Open Innovation (Chesbrough, 2003) and Web 2.0 (O'Reilly, 2004) as well as Living Labs operating as a User Centred Open Innovation Ecosystem (Pallot, 2009), promote a more proactive role of users in the RD Sanders, 2008) and later introduced in the domain of Living Lab research (Mulder & Stappers, 2009). It also discusses the links with existing theories such as Social Capital Theory (Nahapiet and Ghoshal, 1998) and Social Cognitive Theory (Bandura, 1986) as well as Socio-Emotional Intelligence Theory (Goleman, 1998). It also explores the creation of User Group Experience concept for bringing the socio-emotional perspective (Norman, 1995; 1998; 2004; 207; Goleman, 1998) into User Experience (Fleming, 1998) that appears too much focusing on individual users and usability.