User modeling

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

The demographics of web search

Abstract: How does the web search behavior of "rich" and "poor" people differ? Do men and women tend to click on difffferent results for the same query? What are some queries almost exclusively issued by African Americans? These are some of the questions we address in this study. Our research combines three data sources: the query log of a major US-based web search engine, profile information provided by 28 million of its users (birth year, gender and ZIP code), and US-census information including detailed demographic information aggregated at the level of ZIP code. Through this combination we can annotate each query with, e.g. the average per-capita income in the ZIP code it originated from. Though conceptually simple, this combination immediately creates a powerful user modeling tool. The main contributions of this work are the following. First, we provide a demographic description of a large sample of search engine users in the US and show that it agrees well with the distribution of the US population. Second, we describe how different segments of the population differ in their search behavior, e.g. with respect to the queries they formulate or the URLs they click. Third, we explore applications of our methodology to improve web search relevance and to provide better query suggestions. These results enable a wide range of applications including improving web search and advertising where, for instance, targeted advertisements for "family vacations" could be adapted to the (expected) income.

Method for creating user profiles

Abstract: A method for creating personalized user profiles using a client computer. A client computer executes a method which monitors user activities and collects content and context information based on the monitored user activities. The client computer processes the content and context information to determine concepts of interest to the user and the user's level of interest in the concepts. Information related to the concepts and the user's interest level associated with the concepts is used to create a personalized profile for the user on the client computer.

Knowledge Based Recommender Systems Using Explicit User Models

Abstract: While recommender systems are in widespread use, they still experience problems. Many recommender systems produce recommendations which the customers find unsatisfactory. Further, these systems often suffer from problems when there are not enough participants, or when new products enter the system. We perceive an opportunity for knowledge-based recommender systems to gain leverage on recommendation tasks by using explicit models of both the user of the system and the products being recommeded. This differs from previous systems which, when they use a user model, have used one that is inferred from the ratings given by that user (i.e., an implicit user model). We believe that the additional information given by the user and product models can give the system leverage in difficult recommendation tasks, and also alleviate both the "early rater" problem and the "sparse ratings" problem experienced by current recommender systems~

Adaptive Human Computer Interface (AAHCI)

Abstract: An Adaptive Human-Computer Interface (AAHCI) allows an electronic system to automatically monitor and learn from normal in-use behavior exhibited by a human user via responses generated by the supported input devices and to adjust output to the supported output devices accordingly. This Auto-Learning process is different than computer-directed training sessions and takes place as the user begins to use the device for the first time and with repeated use over time. The purpose of AHCI is to provide a user experience that is tailored to the skills, preferences, deficiencies and other personal attributes of the user automatically via machine-learned processes. This in turn provides an improved user experience that is more productive and cost efficient and that can automatically optimize itself over time with repeated use.

Multi-level user help

Abstract: A method for displaying multi-level help for an element of a computer system through creating a hierarchically organized plurality of help levels for the element in which each help level includes an associated help item. The computer system then receives a help activation command for that element from the user and responds by displaying a help item for the element of an initial help level. A user may also edit the help item or create a new help item for one of the plurality of help levels or a new help level. Additionally, each user of the system may edit and create user specific help items, and these help items may be created, edited and implemented “on the fly.”