A Practical Activity Capture Framework for Personal, Lifetime User Modeling

Abstract

Abstract. This paper addresses the problem of capturing rich, longterm personal activity logs of users' interactions with their workstations, for the purpose of deriving predictive, personal user models. Our architecture addresses a number of practical problems with activity capture, including incorporating heterogeneous information from different applications, measuring phenomena with different rates of change, efficiently scheduling knowledge sources, incrementally evolving knowledge representations, and incorporating prior knowledge to combine low-level observations into interpretations better suited for user modeling tasks. We demonstrate that the computational and memory demands of general activity capture are well within reasonable limits even on today's hardware and software platforms.

An integrated personalization framework for SaaS-based cloud services

“…The Profile Observer loads, manages and transforms user information including user profiles, user preferences and usage records. It is responsible for receiving user preference data from the Edge Component and retrieving the state information and behaviors from the surrounding environment [22].…”

Exploiting the User Interaction Context for Automatic Task Detection

“…Simple operating system and application events initiated by the user while interacting with her desktop are recorded by context observers, acting as event object sources. Our use of context observers is similar to the approach followed by contextual attention metadata (Wolpers et al, 2007) and other context observation approaches (Dragunov et al, 2005;Van Kleek and Shrobe, 2007). Context observers, also referred to as context sensors, are programs, macros or plug-ins that can be distinguished based on the origin of the data they deliver.…”

“…Various research areas have already emphasized the use of contextual information as one of the key elements for enhancing current applications. Examples can be cited in personal information management (Sauermann et al, 2005;Catarci et al, 2007;Chernov et al, 2008;Jones et al, 2008), user modeling (Van Kleek and Shrobe, 2007), information retrieval (Callan et al, 2007;Tang et al, 2007;Mylonas et al, 2008), technology-enhanced learning (Schmidt, 2005;Wolpers et al, 2007), etc.…”

Personal Information Management

“…whenever a new location observation's subject is some person and that location observation's value is some place set that person's current location to that place whenever a new web page view event's page is some web page and that web page view event's subject is some person set that person's currently viewing site to that web page several actions have non-web destinations. For example, actions affecting the user's local machine, such as the "play media" "show document" action, are made through PLUM [25]. Still other actions, such as "filter notes" pertain to actions affecting Firefox extensions, such as List.It.…”