Fabian Bohnert

Short biography

Fabian Bohnert is a Ph.D. candidate at Monash University (Melbourne, Australia). His current research towards his Ph.D. is based in the fields of user modelling and personalisation. He is particularly interested in adaptive statistical techniques for user modelling and recommendation in physical spaces with organised information. Fabian holds a Diplom in econo-mathematics (eq. master in econo-mathematics) from Ulm University (Ulm, Germany), with majors data mining and actuarial studies. When undertaking his Diplom thesis and during internships in industry, his research involved probabilistic predictive data modelling and data mining.

Research interests

Visitors to physical educational environments, such as museums, galleries or zoos, are often overwhelmed by the information available in the space they are exploring. Typically being limited in receptivity and time, they are confronted with the challenge of finding and selecting the personally interesting items to view within the available time. Mobile electronic handheld guides can provide guidance and support a visitor in this selection process by identifying and recommending items that match his/her interests. However, recommendation generation from non-intrusive observations of a visitor's behaviour in physical spaces has challenges of its own. Factors such as the spatial layout of the environment and suggested order of item access must be taken into account, as they constrain the recommendation process. I am investigating adaptive user modelling and personalisation approaches that consider such and other constraints.

My advisors are Prof Ingrid Zukerman (Monash University) and Prof Liz Sonenberg (The University of Melbourne).

I am a member of the User Modeling and Natural Language Group (UMNL), which is affiliated with the Centre for Research in Intelligent Systems (CRIS). The UMNL group conducts research in the areas of user modelling and natural language processing, focusing on statistical and machine learning techniques.

Additionally, I am involved in the Kubadji Project. The project is developing user modelling and language technologies to support the creation of personalised electronic handheld guides for museums. Key processes under investigation are (1) the inference of a visitor's interests and activities from non-intrusive observations of his/her behaviour in the physical museum space, (2) the recommendation of items of interest, and (3) the personalisation of the content delivered for these items via the handheld device.

I am a member of the ARC Research Network in Human Communication Science (HCSNet), the ARC Research Network in Enterprise Information Structure (EII), and the EII Taskforce on Context-Aware Computing. I am also a member of the Institute of Electrical and Electronics Engineers (IEEE) and the IEEE Computer Society.

Publications

• Fabian Bohnert and Ingrid Zukerman: "Using Keyword-Based Approaches to Adaptively Predict Interest in Museum Exhibits". In Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI-09), pages 656–665, Melbourne, VIC, Australia, 2009. To appear. [abstract +/-] [bibtex]
  Advances in mobile computing and user modelling have enabled technologies that help museum visitors select personally interesting exhibits to view. This is done by generating personalised exhibit recommendations on the basis of non-intrusive observations of visitors' behaviour in the physical museum space. We describe a simple methodology for manually annotating museum exhibits with bags of keywords (viewed as item features), and present two personalised keyword-based models for predicting a visitor's viewing times of unseen exhibits from his/her viewing times at visited exhibits (viewing time is indicative of interest). Our models were evaluated with a real-world dataset of visitor pathways collected by tracking visitors in a museum. Both models achieve a higher predictive accuracy than a non-personalised baseline, and perform at least as well as a nearest-neighbour collaborative filter.
• Fabian Bohnert, Daniel F. Schmidt, and Ingrid Zukerman: "Spatial Processes for Recommender Systems". In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), pages 2022–2027, Pasadena, CA, USA, 2009. [abstract +/-] [abstract] [pdf] [pdf] [bibtex]
  Spatial processes are typically used to analyse and predict geographic data. This paper adapts such models to predicting a user's interests (i.e., implicit item ratings) within a recommender system in the museum domain. We present the theoretical framework for a model based on Gaussian spatial processes, and discuss efficient algorithms for parameter estimation. Our model was evaluated with a real-world dataset collected by tracking visitors in a museum, attaining a higher predictive accuracy than state-of-the-art collaborative filters.
• Fabian Bohnert, Ingrid Zukerman, and Daniel F. Schmidt: "Using Gaussian Spatial Processes to Model and Predict Interests in Museum Exhibits". In Proceedings of the Seventh Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP-09), held in conjunction with the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), pages 13–19, Pasadena, CA, USA, 2009. [abstract +/-] [pdf] [pdf] [bibtex]
  This paper adapts models from the area of spatial statistics to the task of predicting a user's interests (i.e., implicit item ratings) within a recommender system in the museum domain. We develop a model based on Gaussian spatial processes, and discuss two ways of computing item-to-item distances in the museum setting. Our model was evaluated with a real-world dataset collected by tracking visitors in a museum. Overall, our model attains a higher predictive accuracy than nearest-neighbour collaborative filters. In addition, the model variant using physical distances outperforms that using distances computed from item-to-item similarities.
• Fabian Bohnert and Ingrid Zukerman: "Non-Intrusive Personalisation of the Museum Experience". In Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP-09), pages 197–209, Trento, Italy, 2009. Best Student Paper Award. [abstract +/-] [pdf] [bibtex]
  The vast amount of information presented in museums is often overwhelming to a visitor, making it difficult to select personally interesting exhibits. Advances in mobile computing and user modelling have made possible technology that can assist a visitor in this selection process. Such a technology can (1) utilise non-intrusive observations of a visitor's behaviour in the physical space to learn a model of his/her interests, and (2) generate personalised exhibit recommendations based on interest predictions. Due to the physicality of the domain, datasets of visitors' behaviour (i.e., visitor pathways) are difficult to obtain prior to deploying mobile technology in a museum. However, they are necessary to assess different modelling techniques. This paper reports on a methodology that we used to conduct a manual data collection, and describes the dataset we obtained. We also present two collaborative models for predicting a visitor's viewing times of unseen exhibits from his/her viewing times at visited exhibits (viewing time is indicative of interest), and evaluate our models with the dataset we collected. Both models achieve a higher predictive accuracy than a non-personalised baseline.
• Fabian Bohnert and Ingrid Zukerman: "A Computer-Supported Methodology for Recording and Visualising Visitor Behaviour in Museums". In Adjunct Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP-09), pages 115–120, Trento, Italy, 2009. [abstract +/-] [pdf] [bibtex]
  Developing user modelling and personalisation techniques for museums requires datasets about visitor behaviour in the physical museum space (i.e., visitor pathways). Advancing traditional paper-based tracking techniques, this demonstration presents a computer-supported methodology for (1) observationally recording visitors' time-annotated pathways in physical museums, and (2) post-collection processing and visualising the gathered data. Our methodology was used to collect and post-process a dataset of more than 170 pathways of visitors to Melbourne Museum (Melbourne, Australia). In this paper, we report on our data collection and the lessons learnt from it.
• Fabian Bohnert, Daniel F. Schmidt, and Ingrid Zukerman: "Spatial Processes for Recommender Systems", Technical report 2009/238, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia, 2009. [abstract +/-] [abstract] [pdf] [bibtex]
  Spatial processes are typically used to analyse and predict geographic data. This paper adapts such models to the prediction of a user's interests or item ratings in recommender systems. We present the theoretical framework for a model based on Gaussian spatial processes, and discuss efficient algorithms for parameter estimation. Our model was evaluated with simulated data and a real-world dataset collected by tracking visitors in a museum, and achieves a higher predictive accuracy than a non-personalised baseline. Additionally, in the real-world scenario, the model attains a higher predictive accuracy than state-of-the-art collaborative filters.
• Fabian Bohnert, Ingrid Zukerman, Shlomo Berkovsky, Timothy Baldwin, and Liz Sonenberg: "Using Interest and Transition Models to Predict Visitor Locations in Museums". In AI Communications 21(2-3) (AICom) – Special Issue on Recommender Systems, pages 195–202, 2008. [abstract +/-] [pdf] [bibtex]
  Museums offer vast amounts of information, but a visitor's receptivity and time are typically limited, providing the visitor with the challenge of selecting the (subjectively) interesting exhibits to view within the available time. Mobile, electronic handheld guides offer the opportunity to improve a visitor's experience by recommending exhibits of interest, and adapting the delivered content. The first step in this personalisation process is the prediction of a visitor's activities and interests. In this paper we study non-intrusive, adaptive user modelling techniques that take into account the physical constraints of the exhibition layout. We present two collaborative models for predicting a visitor's next locations in a museum, and an ensemble model that combines the predictions of these models. The three models were trained and tested on a small dataset of museum visits. Our results are encouraging, with the ensemble model yielding the best performance overall.
• Karl Grieser, Timothy Baldwin, Fabian Bohnert, and Liz Sonenberg: "Using Collaboratively Constructed Document Collections to Simulate Real-World Object Comparisons". In Proceedings of the 13th Australasian Document Computing Symposium (ADCS-08), pages 73–76, Hobart, TAS, Australia, 2008. [abstract +/-] [pdf] [bibtex]
  While the layout of a museum exhibition is largely prescribed by the curator, visitors to museums view connections between exhibits in ways unique to themselves. With the assistance of a large-scale survey of museum visitors we identify that the view taken by museum visitors of a collection of exhibits can be represented by similarity over documents associated with each exhibit. We show that even when using a basic document similarity measure there is a correlation between document similarity and visitors' judgements of relatedness of exhibits aligned to these documents.
• Fabian Bohnert: "Constraint-Aware User Modelling and Recommendation in Physical Environments such as Museums". Abstract presented at the HCSNet Workshop on Embodied Interaction in Mobile, Physical and Virtual Environments, Sydney, NSW, Australia, 2008. [abstract +/-] [abstract] [bibtex]
  Visitors to physical educational environments, such as museums, galleries or zoos, are often overwhelmed by the information available in the space they are exploring. Typically being limited in receptivity and time, they are confronted with the challenge of finding and selecting the personally interesting items to view within the available time. A personal human guide could support a visitor in this selection process, but the provision of personal guides is generally not possible. Advances in mobile technologies and user modelling point towards an alternative solution: personalised electronic handheld guides.
  
  Personalised electronic handheld guides can provide guidance and support a visitor in the process of selecting suitable items by identifying and recommending items that match his/her interests. Additionally, they have the potential to infer these interests by tracking a visitor's behaviour within the environment. However, recommendation generation from non-intrusive observations of a visitor's behaviour in a physical space has challenges of its own. For example, as items have informational dependencies suggesting a certain order of access, careful thought is usually put into placing the items into the physical space to enable a coherent experience. Hence, a visitor's behaviour is influenced by both the suggested order of item access and the spatial layout of the environment, and consequently, these factors must be considered when modelling a visitor's interests from non-intrusive observations of his/her movements through the space. They must also be taken into account when generating and delivering recommendations. Therefore, the overall personalisation process is constrained by such factors. This research investigates adaptive user modelling and recommendation approaches that consider such and other constraints.
• Fabian Bohnert: "Adaptive User Modelling and Recommendation in Constrained Physical Environments". Poster presented at the Monash 2008 Research Month Poster Exhibition (18/08/2008 - 04/09/2008), also displayed at the Monash e-Research Centre 2008 e-XPO Exhibition (19/08/2008 - 25/08/2008), Melbourne, VIC, Australia, 2008. Outstanding Poster Award. [abstract +/-] [jpg] [bibtex]
  Visitors to physical educational environments, such as museums, are often overwhelmed by the information available in the space they are exploring. They are confronted with the challenge of finding the personally interesting items to view within the available time. Mobile electronic guides can provide guidance and point to relevant information by identifying and recommending items that match a visitor's interests. However, recommendation generation from non-intrusive observations of a visitor's behaviour in physical spaces has challenges of its own. Factors such as the spatial layout of the environment and suggested order of item access must be taken into account, as they constrain the recommendation process. This research investigates adaptive user modelling and personalisation approaches that consider such and other constraints.
• Fabian Bohnert, Ingrid Zukerman, Shlomo Berkovsky, Timothy Baldwin, and Liz Sonenberg: "Using Collaborative Models to Adaptively Predict Visitor Locations in Museums". In Proceedings of the Fifth International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH-08), pages 42–51, Hanover, Germany, 2008. [abstract +/-] [bibtex]
  The vast amounts of information presented in museums can be overwhelming to a visitor, whose receptivity and time are typically limited. Hence, s/he might have difficulties selecting interesting exhibits to view within the available time. Mobile, context-aware guides offer the opportunity to improve a visitor's experience by recommending exhibits of interest, and personalising the delivered content. The first step in this recommendation process is the accurate prediction of a visitor's activities and preferences. In this paper, we present two adaptive collaborative models for predicting a visitor's next locations in a museum, and an ensemble model that combines their predictions. Our experimental results from a study using a small dataset of museum visits are encouraging, with the ensemble model yielding the best performance overall.
• Fabian Bohnert: "Adaptive User Modelling and Recommendation in Constrained Physical Environments". In Proceedings of the Fifth International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH-08), pages 378–383, Hanover, Germany, 2008. [abstract +/-] [bibtex]
  Visitors to physical educational environments, such as museums, are often overwhelmed by the information available in the space they are exploring. They are confronted with the challenge of finding personally interesting items to view in the available time. Electronic mobile guides can provide guidance and point to relevant information by identifying and recommending items that match a visitor's interests. However, recommendation generation in physical spaces has challenges of its own. Factors such as the spatial layout of the environment and suggested order of item access must be taken into account, as they constrain the recommendation process. This research investigates adaptive user modelling and personalisation approaches that consider such and other constraints.
• Fabian Bohnert: "Constraint-Aware User Modelling and Personalisation in Physical Environments". In Adjunct Proceedings of the Sixth International Conference on Pervasive Computing (Pervasive-08), pages 167–172, Sydney, NSW, Australia, 2008. [abstract +/-] [pdf] [bibtex]
  The vast amounts of information presented in physical educational spaces such as museums are often overwhelming to a visitor, whose receptivity and time are typically limited. Hence, s/he might have difficulties selecting the personally interesting items to view within the available time. Mobile electronic guides can support a visitor in this selection process by identifying and recommending items that match his/her interests. However, recommendation generation in physical spaces has challenges of its own. Factors such as the spatial layout of the environment and suggested order of item access must be taken into account, as they constrain the recommendation process. This research investigates adaptive user modelling and personalisation approaches that consider such and other constraints.
• Fabian Bohnert and Ingrid Zukerman: "The Kubadji Project: Adaptive User Modelling and Personalisation for Improving the Museum Experience". Abstract presented at the Joint HCSNet-EII Workshop on Interactive and Ubiquitous Information Access, Sydney, NSW, Australia, 2008. [abstract +/-] [bibtex]
  Museums offer vast amounts of information, but a visitor's receptivity and time are typically limited — confronting the visitor with the challenge of selecting the (subjectively) interesting content to view within the available time. A personal human guide could support the visitor in this selection process, but the provision of personal guides is generally impractical. Advances in mobile, context-aware technologies and user modelling point towards an alternative solution: personalised electronic handheld guides. They offer the opportunity to improve a visitor's experience by recommending items of interest, and personalising the delivered content.
  
  However, user modelling and recommendation generation in physical museum spaces have challenges of their own. For instance, factors such as the spatial layout of the environment and suggested order of item access must be taken into account, as they constrain the recommendation process. The Kubadji project (http://www.kubadji.org/) is investigating user modelling and personalisation approaches that consider such and other constraints. Additionally, in order to deliver personalised content for items of interest, user modelling and personalisation techniques are combined with natural language processing techniques.
  
  In summary, the Kubadji project researches user modelling and language technologies to support the creation of personalised electronic handheld guides for museums. Key processes under investigation are (1) the inference of a visitor's interests from non-intrusive observations of his/her behaviour in the physical museum space, (2) the recommendation of items of interest, and (3) the personalisation of the content delivered for these items via the handheld device.
• Fabian Bohnert, Ingrid Zukerman, Shlomo Berkovsky, Timothy Baldwin, and Liz Sonenberg: "Using Interest and Transition Models to Predict Visitor Locations in Museums", Technical report 2008/219, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia, 2008. [abstract +/-] [abstract] [pdf] [bibtex]
  Museums offer vast amounts of information, but a visitor's receptivity and time are typically limited — providing the visitor with the challenge of selecting the (subjectively) interesting exhibits to view within the time available. Mobile, context-aware computer systems offer the opportunity to improve a visitor's experience by recommending exhibits of interest, and personalising the delivered content. A first step in this process is the prediction of a visitor's activities and interests. In this paper we study non-intrusive, adaptive user modelling techniques that include consideration of the physical constraints of the exhibition layout. We present two collaborative models for predicting a visitor's locations in a museum, and an ensemble model that combines their predictions. These models were trained and tested on a small dataset of museum visits. Our results are encouraging, with the ensemble model yielding the best performance overall.
• Fabian Bohnert and Ingrid Zukerman: "Using Viewing Time for Theme Prediction in Cultural Heritage Spaces". In Proceedings of the 20th Australasian Joint Conference on Artificial Intelligence (AI-07), pages 367–376, Gold Coast, QLD, Australia, 2007. [abstract +/-] [bibtex]
  Visitors to cultural heritage sites are often overwhelmed by the information available in the space they are exploring. The challenge is to find items of relevance in the limited time available. Mobile computer systems can provide guidance and point to relevant information by identifying and recommending content that matches a user's interests. In this paper we infer implicit ratings from observed viewing times, and outline a collaborative user modelling approach to predict a user's interests and expected viewing times. We make predictions about viewing themes (item sets) taking into account the visitor's time limit. Our model based on relative interests with imputed ratings yielded the best performance.

Other links

• UMAP 2010 – 18th International Conference on User Modeling, Adaptation, and Personalization, Big Island of Hawaii, Hawaii, USA

• CRIS postgrad lunch – a weekly seminar series by and for HDR students

• Stuttgart, Germany – the place where I grew up
• Ulm, Germany – the place where I studied
• Melbourne, Australia – the place where I live