User modeling

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

User interface plasticity: model driven engineering to the limit!

Abstract: Ten years ago, I introduced the notion of user interface plasticity to denote the capacity of user interfaces to adapt, or to be adapted, to the context of use while preserving usability. The Model Driven Engineering (MDE) approach, which was used for user interface generation since the early eighties in HCI, has recently been revived to address this complex problem. Although MDE has resulted in interesting and convincing results for conventional WIMP user interfaces, it has not fully demonstrated its theoretical promises yet. In this paper, we discuss how to push MDE to the limit in order to reconcile high-level modeling techniques with low-level programming in order to go beyond WIMP user interfaces.

User Modeling for a Personal Assistant

Abstract: We present a user modeling system that serves as the foundation of a personal assistant. The system ingests web search history for signed-in users, and identifies coherent contexts that correspond to tasks, interests, and habits. Unlike past work which focused on either in-session tasks or tasks over a few days, we look at several months of history in order to identify not just short-term tasks, but also long-term interests and habits. The features we use for identifying coherent contexts yield substantially higher precision and recall than past work. We also present an algorithm for identifying contexts that is 8 to 30 times faster than previous algorithms. The user modeling system has been deployed in production. It runs over hundreds of millions of users, and updates the models with a 10-minute latency. The contexts identified by the system serve as the foundation for generating recommendations in Google Now.

Method and system for dynamic advertising based on user actions

Abstract: A method and system for dynamically responding to advertisement reactions of a user in social network that includes serving an initial advertisement to a user of a social network; gathering a response action of the user associated with the initial advertisement; categorizing a quality of the response action of the user; creating an advertiser response based on the quality of the response action; and sending the response to the user.

User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items

Abstract: Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Noncollaborative user modeling techniques that rely on the item features can be used to recommend new items. However, they only use the past preferences of each user to provide recommendations for that user. They do not utilize information from the past preferences of other users, which can potentially be ignoring useful information. More recent factor models transfer knowledge across users using their preference information in order to provide more accurate recommendations. These methods learn a low-rank approximation for the preference matrix, which can lead to loss of information. Moreover, they might not be able to learn useful patterns given very sparse datasets. In this work, we present UFSM, a method for top-n recommendation of new items given binary user preferences. UFSM learns User-specific Feature-based item-Similarity Models, and its strength lies in combining two points: (1) exploiting preference information across all users to learn multiple global item similarity functions and (2) learning user-specific weights that determine the contribution of each global similarity function in generating recommendations for each user. UFSM can be considered as a sparse high-dimensional factor model where the previous preferences of each user are incorporated within his or her latent representation. This way, UFSM combines the merits of item similarity models that capture local relations among items and factor models that learn global preference patterns. A comprehensive set of experiments was conduced to compare UFSM against state-of-the-art collaborative factor models and noncollaborative user modeling techniques. Results show that UFSM outperforms other techniques in terms of recommendation quality. UFSM manages to yield better recommendations even with very sparse datasets. Results also show that UFSM can efficiently handle high-dimensional as well as low-dimensional item feature spaces.

A Discussion of Privacy Challenges in User Profiling with Big Data Techniques: The EEXCESS Use Case

Abstract: User profiling is the process of collecting information about a user in order to construct their profile The information in a user profile may include various attributes of a user such as geographical location, academic and professional background, membership in groups, interests, preferences, opinions, etc Big data techniques enable collecting accurate and rich information for user profiles, in particular due to their ability to process unstructured as well as structured information in high volumes from multiple sources Accurate and rich user profiles are important for applications such as recommender systems, which try to predict elements that a user has not yet considered but may find useful The information contained in user profiles is personal and thus there are privacy issues related to user profiling In this position paper, we discuss user profiling with big data techniques and the associated privacy challenges We also discuss the ongoing EU-funded EEXCESS project as a concrete example of constructing user profiles with big data techniques and the approaches being considered for preserving user privacy