User modeling

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

User modeling in intelligent information retrieval

Abstract: The issue of exploiting user modeling techniques in the framework of cooperative interfaces to complex artificial systems has recently received increasing attention. In this paper we present the IR-NLI II system, an expert interface that allows casual users to access online information retrieval systems and encompasses user modeling capabilities. More specifically, an illustration of the user modeling subsystem is given by describing the organization of the user model proposed for the particular application area, together with its use during system operation. The techniques utilized for the construction of the model are presented as well. They are based on the use of stereotypes, which are descriptions of typical classes of users. More specifically, they include both declarative and procedural knowledge for describing the features of the class to which the stereotype is related, for assigning a user to that class, and for acquiring and validating the necessary information during system operation.

Social Attentional Memory Network: Modeling Aspect- and Friend-Level Differences in Recommendation

Abstract: Social connections are known to be helpful for modeling users' potential preferences and improving the performance of recommender systems. However, in social-aware recommendations, there are two issues which influence the inference of users' preferences, and haven't been well-studied in most existing methods: First, the preferences of a user may only partially match that of his friends in certain aspects, especially when considering a user with diverse interests. Second, for an individual, the influence strength of his friends might be different, as not all friends are equally helpful for modeling his preferences in the system. To address the above issues, in this paper, we propose a novel Social Attentional Memory Network (SAMN) for social-aware recommendation. Specifically, we first design an attention-based memory module to learn user-friend relation vectors, which can capture the varying aspect attentions that a user share with his different friends. Then we build a friend-level attention component to adaptively select informative friends for user modeling. The two components are fused together to mutually enhance each other and lead to a finer extended model. Experimental results on three publicly available datasets show that the proposed SAMN model consistently and significantly outperforms the state-of-the-art recommendation methods. Furthermore, qualitative studies have been made to explore what the proposed attention-based memory module and friend-level attention have learnt, which provide insights into the model's learning process.

Methods, systems, and computer program products for facilitating user choices among complex alternatives using conjoint analysis

Abstract: Methods, systems, and computer program products for facilitating user choices among complex alternatives utilize conjoint analysis to simplify choices to be made by the user. A selector tool presents a user with a first and second series of choices relating to attributes of products or services available to the user. A utilities calculation engine calculates the relative utility of each of the products or services to the user and presents output to the user, which indicates the relative utility of each of the products or services. The user can then select the product or service that has the highest utility value for the user based on the calculated relative utility values.

Visualization and User-Modeling for Browsing Personal Photo Libraries

Abstract: We present a user-centric system for visualization and layout for content-based image retrieval. Image features (visual and/or semantic) are used to display retrievals as thumbnails in a 2-D spatial layout or “configuration” which conveys all pair-wise mutual similarities. A graphical optimization technique is used to provide maximally uncluttered and informative layouts. Moreover, a novel subspace feature weighting technique can be used to modify 2-D layouts in a variety of context-dependent ways. An efficient computational technique for subspace weighting and re-estimation leads to a simple user-modeling framework whereby the system can learn to display query results based on layout examples (or relevance feedback) provided by the user. The resulting retrieval, browsing and visualization can adapt to the user's (time-varying) notions of content, context and preferences in style and interactive navigation. Monte Carlo simulations with machine-generated layouts as well as pilot user studies have demonstrated the ability of this framework to model or “mimic” users, by automatically generating layouts according to their preferences.

User-controllable personalization

Abstract: In this research we investigated the role of user controllability on personalized systems by implementing and studying a novel interactive recommender interface, SetFusion. We examined whether allowing the user to control the process of fusing or integrating different algorithms (i.e., different sources of relevance) resulted in increased engagement and a better user experience. The essential contribution of this research stems from the results of a user study (N=40) of controllability in a scenario where users could fuse different recommendation approaches, with the possibility of inspecting and filtering the items recommended. First, we introduce an interactive Venn diagram visualization, which combined with sliders, can provide an efficient visual paradigm for information filtering. Second, we provide a three-fold evaluation of the user experience: objective metrics, subjective user perception, and behavioral measures. Through the analysis of these metrics, we confirmed results from recent studies, such as the effect of trusting propensity on accepting the recommendations and also unveiled the importance of features such as being a native speaker. Our results present several implications for the design and implementation of user-controllable personalized systems. We explored user-controllable interfaces as extension of traditional-ranked lists.We introduced SetFusion, a controllable interface with sliders and a Venn diagram.We conducted a controlled user study on online conference article recommendation.Our evaluation had three dimensions: users' perception, behavioral and IR metrics.Controllable interface had a positive effect influenced by users' characteristics.