User Model in Personalized Information Filtering

Traditional user models mainly include keywords, which represent user interests, while the enhanced user models keep high-level knowledge representation about users. The user profile is the most important part in the user model. An overview of literature shows that most of the existing filtering solutions adopt a single profile that is built from all user inputs. Therefore, the user model in this dissertation study was formulated through similar method. The user profile is a single monolithic data structure, which contains a list of preferences. Specifically in this study, the preferences are medical topics indicated by users based on their

interests. Some early researchers employed the term “query” to refer to user models (Belkin & Croft, 1992). In this view, a user model is actually a saved query (or a set of saved queries). LyricTime (Loeb, 1992), a music recommendation system, adopted the mood specified by users as a user model. After users were logged in, the system would deliver a playlist based on the selected mood from users. Widyantoro et al. (2000) proposed a more complex user model with a two-fragment user profile, which contained the following components: a long-term interest profile that captures user’s general interests and a short- term interest profile, which kept track of a user’s more recent, faster changing interests. Traditional user models are utilized in commercial sites as well. Amazon employs “favorites” to represent user models and the data of “favorites” are generated from the preferred categories set by users (Brusilovsky et al., 2007).

Recent researchers have paid more attention to the users’ location, activity, or other contextual information in building users’ profiles. Loeb and Panagos (2011) described a mechanism by which a user profile can be constructed in real time from relevant sub-profiles. Each sub-profile corresponds to a specific context (time, location), mood, task, and social context. Moreover, the constructed profile in the researchers’ methods can be updated based on events, feedback, context information, or explicit user updates. In addition to academic study, many commercial applications also add the contextual information in creating profiles. For instance, Amazon.com supports the creation of multiple account profile fragments. Amazon’s system allows users to go back to their personal history and specify which of the items were purchased as gifts for others. In this way, the system can learn that these items specified as gifts do not represent the user’s personal preference but, rather, their friends’ tastes.

The methods of acquiring users’ interests can be explicit or implicit, depending on the source of the data. If the data are generated from user interrogation, the methods are considered explicit. In contrast, implicit approaches often refer to handling the data on observed user behaviors. Modern information filtering (or recommendation) always involves the explicit and implicit approaches simultaneously and sets different weights on the signals of these approaches in the learning module.

User Models Built on Explicit Methods

User interrogation has been widely used and is always considered the more reliable approach in acquiring user knowledge compared to other means. The basis for this method is that conceptually users know themselves better than others who may know them. In the process of interrogation, users are required to provide information on their interest directly to the filtering system. Previous researchers have examined different types of interrogation with variable levels of flexibility granted to users. Some filtering systems present a predefined set of profiles, granting users the ability to choose one from these profiles (McCleary, 1994). This method makes users capable of creating profiles very quickly. To increase the freedom of choice, some systems provide users with a set of terms and ask users to construct their profiles based on these terms. Some systems allow users not only to pick terms but also set the importance of terms in building the profiles (McCleary, 1994). These aforementioned approaches generate user profiles, which are represented by a vector.

User Models Built on Implicit Methods

The implicit approach acquires knowledge from users by analyzing their recorded behaviors in using the systems. The core of the implicit method is to translate user behaviors into user tastes. Due to the influence of user’s contextual environment and personal

emotional status, this process is deemed highly challenging. For instance, users’ browsing actions can be interrupted by some uncontrollable issues, such as phone calls. Despite these difficulties, researchers have achieved positive advances in understanding implicit user behavior. Previous studies indicated that the time users spend reading data items relates to users’ interest in the data (Morita & Shinoda, 1994; Konstan et al., 1997). The work conducted by Morita and Shinoda examined the correlation between the usefulness scores of articles and the time users spent reading them. The study demonstrated that there is a relatively strong correlation between these two factors (correlation coefficient is 0.49), suggesting that users spend more time reading documents that they find relevant than they do reading documents that are irrelevant. Some other types of user behavior can be leveraged to acquire interest information implicitly. Goecks and Shavlik (2000) presented an approach that learned users’ preferences through observing the hyperlinks clicked on and the users’ activity, as tracked by their mouse and scrolling movements. The results were consistent with the surrogate measurements of user interests, indicating the well-designed implicit approach was capable of predicting a user interest profile. Calvi and De Bra (1997) conducted a similar study in which they developed an adaptive learning approach that analyzes users’ past navigation history. This study performed well in the filtering module of educational hypermedia systems.

In addition to promising results reported by researchers, there exists evidence on the limitation of user behaviors in practical applications. Kelly and Belkin (2001) investigated whether an earlier finding on reading time (Morita & Shinoda, 1994) could be replicated in another information retrieval context. In their study, results obtained from practical experiments led to a conclusion that was the opposite of a previous study: no significant

relationship exists between the length of time that a user spends viewing a document and the user’s subsequent judgment on document relevance. This suggests that the theory concerning the relationship between reading time and relevance is scope-specific. In order to explain this clearly, Kelly and Belkin (2004) investigated the effects of tasks on the effectiveness of display time as implicit feedback. They analyzed online information-seeking behaviors of seven subjects during a fourteen-week period. In the study, subjects were asked to identify their tasks, classify the documents that they viewed according to these tasks, and evaluate the usefulness of the documents. The experimental results showed that tasks and the time spent on tasks vary with the settings of the individual studies, resulting in no general, direct relationship between viewing time and usefulness. The effects of tasks on viewing time partially explain the inconsistency in the conclusions of the previous studies.