User modeling

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

Learning geographical preferences for point-of-interest recommendation

Abstract: The problem of point of interest (POI) recommendation is to provide personalized recommendations of places of interests, such as restaurants, for mobile users. Due to its complexity and its connection to location based social networks (LBSNs), the decision process of a user choose a POI is complex and can be influenced by various factors, such as user preferences, geographical influences, and user mobility behaviors. While there are some studies on POI recommendations, it lacks of integrated analysis of the joint effect of multiple factors. To this end, in this paper, we propose a novel geographical probabilistic factor analysis framework which strategically takes various factors into consideration. Specifically, this framework allows to capture the geographical influences on a user's check-in behavior. Also, the user mobility behaviors can be effectively exploited in the recommendation model. Moreover, the recommendation model can effectively make use of user check-in count data as implicity user feedback for modeling user preferences. Finally, experimental results on real-world LBSNs data show that the proposed recommendation method outperforms state-of-the-art latent factor models with a significant margin.

Information Filtering: Overview of Issues, Research and Systems

Abstract: An abundant amount of information is created and delivered over electronic media Users risk becoming overwhelmed by the flow of information, and they lack adequate tools to help them manage the situation Information filtering (IF) is one of the methods that is rapidly evolving to manage large information flows The aim of IF is to expose users to only information that is relevant to them Many IF systems have been developed in recent years for various application domains Some examples of filtering applications are: filters for search results on the internet that are employed in the Internet software, personal e-mail filters based on personal profiles, listservers or newsgroups filters for groups or individuals, browser filters that block non-valuable information, filters designed to give children access them only to suitable pages, filters for e-commerce applications that address products and promotions to potential customers only, and many more The different systems use various methods, concepts, and techniques from diverse research areas like: Information Retrieval, Artificial Intelligence, or Behavioral Science Various systems cover different scope, have divergent functionality, and various platforms There are many systems of widely varying philosophies, but all share the goal of automatically directing the most valuable information to users in accordance with their User Model, and of helping them use their limited reading time most optimally This paper clarifies the difference between IF systems and related systems, such as information retrieval (IR) systems, or Extraction systems The paper defines a framework to classify IF systems according to several parameters, and illustrates the approach with commercial and academic systems The paper describes the underlying concepts of IF systems and the techniques that are used to implement them It discusses methods and measurements that are used for evaluation of IF systems and limitations of the current systems In the conclusion we present research issues in the Information Filtering research arena, such as user modeling, evaluation standardization and integration with digital libraries and Web repositories

Learning method and system based on questioning

Abstract: An apparatus and a system to teach a user a subject based on his questions. The system allows the user to control his learning process, and helps to fill in gaps of misunderstanding in the subject. In one embodiment, the system, including a database, presents study materials on the subject to the user. After working on the presented materials, the user enters his question into the system, which generates an answer to the question, and presents it to him. Then the system compares the question with one or more questions previously entered by the user to determine his understanding level in the subject. Based on the determination, the system may present to the user appropriate study materials. The user typically asks more than one question, and the process of answering his question by the system repeats.

AHA! An open Adaptive Hypermedia Architecture

Abstract: Hypermedia applications generate comprehension and orientation problems due to their rich link structure. Adaptive hypermedia tries to alleviate these problems by ensuring that the links that are offered and the content of the information pages are adapted to each individual user. This is done by maintaining a user model. Most adaptive hypermedia systems are aimed at one specific application. They provide an engine for maintaining the user model and for adapting content and link structure. They use a fixed screen layout that may include windows (HTML frames) for an annotated table of contents, an overview of known or missing knowledge, etc. Such systems are typically closed and difficult to reuse for very different applications. We present AHA, an open Adaptive Hypermedia Architecture that is suitable for many different applications. This paper concentrates on the adaptive hypermedia engine, which maintains the user model and which filters content pages and link structures accordingly. The engine...

Machine Learning for User Modeling

Abstract: At first blush, user modeling appears to be a prime candidate for straightforward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labeled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.