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Papers by Matthias Braunhofer

Lecture Notes in Computer Science, 2014

Information Technology & Tourism, 2017

Information and Communication Technologies in Tourism 2016, 2016

Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14, 2014

Lecture Notes in Computer Science, 2014

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization - UMAP '16, 2016

User Modeling and User-Adapted Interaction, 2016

The new user problem in recommender systems is still challenging , and there is not yet a unique ... more The new user problem in recommender systems is still challenging , and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering that are based on the exploitation of user personality information: (a) personality-based collaborative filtering, which directly improves the recommendation prediction model by incorporating user personality information; (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user; and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6% to 94% for users completely new to the system, while increasing the novelty of the recommended items by 3% to 40% with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.

Proceedings of the 8th Acm Conference, Oct 6, 2014

Lecture Notes in Business Information Processing, 2013

Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct - MobileHCI '15, 2015

Information and Communication Technologies in Tourism 2015, 2014

Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14, 2014

Proceedings of the fifth ACM conference on Recommender systems - RecSys '11, 2011

Context-aware music recommender systems suggest music items taking into consideration contextual ... more Context-aware music recommender systems suggest music items taking into consideration contextual conditions, such as the user mood or location, that may influence the user preferences at a particular moment. In this paper we consider a particular kind of context-aware recommendation task: selecting music suited for a place of interest (POI), which the user is visiting, and that is illustrated in a mobile travel guide. We have designed an approach for this novel recommendation task by matching music to POIs using emotional tags. In order to test our approach, we have developed a mobile application that suggests an itinerary and plays recommended music for each visited POI. The results of the study show that users judge the recommended music suited for the POIs, and the music is rated higher when it is played in this usage scenario.

Information and Communication Technologies in Tourism 2014, 2013

International Journal of Multimedia Information Retrieval, 2013

The increasing amount of online music content has opened opportunities for implementing new effec... more The increasing amount of online music content has opened opportunities for implementing new effective information access services-commonly known as music recommender systems-that support music navigation, discovery, sharing, and formation of user communities. In recent years, a new research area of contextual music recommendation and retrieval has emerged. Context-aware music recommender systems are capable of suggesting music items taking into consideration contextual conditions, such as the user's mood or location, that may influence the user's preferences at a particular moment. In this work, we consider a particular kind of context-aware recommendation taskselecting music content that fits a place of interest (POI). To address this problem we have used emotional tags assigned to both music tracks and POIs, and we have considered a set of similarity metrics for tagged resources to establish a match between music tracks and POIs. Following an initial webbased evaluation of the core matching technique, we have developed a mobile application that suggests an itinerary and plays recommended music for each visited POI, and evaluated it in a live user study. The results of the study show that users judge the recommended music as suited for the POIs, and that the music is rated higher when it is played in this usage scenario.

Lecture Notes in Computer Science, 2013

Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossib... more Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the user's personality -using the Five Factor Model (FFM) -in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, contextaware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.

In this demo paper we present a novel context-aware mobile recommender system for places of inter... more In this demo paper we present a novel context-aware mobile recommender system for places of interest (POIs). Unlike existing systems, which learn users' preferences solely from their past ratings, it considers also their personality -using the Five Factor Model. Personality is acquired by asking users to complete a brief and entertaining questionnaire as part of the registration process, and is then exploited in:

In this paper we present STS (South Tyrol Suggests), a context-aware mobile recommender system fo... more In this paper we present STS (South Tyrol Suggests), a context-aware mobile recommender system for places of interest (POIs) that integrates some innovative components, including: a personality questionnaire, i.e., a brief and entertaining questionnaire used by the system to learn users' personality; an active learning module that acquires ratings-in-context for POIs that users are likely to have experienced; and a matrix factorization based recommendation module that leverages the personality information and several contextual factors in order to generate more relevant recommendations. Adopting a system oriented perspective, we describe the assessment of the combination of the implemented components. We focus on usability aspects and report the end-user assessment of STS. It was obtained from a controlled live user study as well as from the log data produced by a larger sample of users that have freely downloaded and tried STS through Google Play Store. The result of the assessment showed that the overall usability of the system falls between "good" and "excellent", it helped us to identify potential problems and it provided valuable indications for future system improvement.

Nowadays, Recommender Systems (RSs) play a key role in many businesses. They provide consumers wi... more Nowadays, Recommender Systems (RSs) play a key role in many businesses. They provide consumers with relevant recommendations, e.g., Places of Interest (POIs) to a tourist, based on user preference data, mainly in the form of ratings for items. The accuracy of recommendations largely depends on the quality and quantity of the ratings (preferences) provided by the users. However, users often tend to rate no or only few items, causing low accuracy of the recommendation. Active Learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire a larger number of high-quality ratings (preferences), and hence, improve the recommendation accuracy. In this paper, we propose a personalized active learning approach that leverages user's personality data to get more and better in-context ratings. We have designed a novel human computer interaction and assessed our proposed approach in a live user study -which is not common in active learning research. The main result is that the system is able to collect better ratings and provide more relevant recommendations compared to a variant that is using a state of the art approach to preference acquisition.

Lecture Notes in Computer Science, 2014

Information Technology & Tourism, 2017

Information and Communication Technologies in Tourism 2016, 2016

Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14, 2014

Lecture Notes in Computer Science, 2014

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization - UMAP '16, 2016

User Modeling and User-Adapted Interaction, 2016

The new user problem in recommender systems is still challenging , and there is not yet a unique ... more The new user problem in recommender systems is still challenging , and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering that are based on the exploitation of user personality information: (a) personality-based collaborative filtering, which directly improves the recommendation prediction model by incorporating user personality information; (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user; and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6% to 94% for users completely new to the system, while increasing the novelty of the recommended items by 3% to 40% with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.

Proceedings of the 8th Acm Conference, Oct 6, 2014

Lecture Notes in Business Information Processing, 2013

Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct - MobileHCI '15, 2015

Information and Communication Technologies in Tourism 2015, 2014

Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14, 2014

Proceedings of the fifth ACM conference on Recommender systems - RecSys '11, 2011

Context-aware music recommender systems suggest music items taking into consideration contextual ... more Context-aware music recommender systems suggest music items taking into consideration contextual conditions, such as the user mood or location, that may influence the user preferences at a particular moment. In this paper we consider a particular kind of context-aware recommendation task: selecting music suited for a place of interest (POI), which the user is visiting, and that is illustrated in a mobile travel guide. We have designed an approach for this novel recommendation task by matching music to POIs using emotional tags. In order to test our approach, we have developed a mobile application that suggests an itinerary and plays recommended music for each visited POI. The results of the study show that users judge the recommended music suited for the POIs, and the music is rated higher when it is played in this usage scenario.

Information and Communication Technologies in Tourism 2014, 2013

International Journal of Multimedia Information Retrieval, 2013

The increasing amount of online music content has opened opportunities for implementing new effec... more The increasing amount of online music content has opened opportunities for implementing new effective information access services-commonly known as music recommender systems-that support music navigation, discovery, sharing, and formation of user communities. In recent years, a new research area of contextual music recommendation and retrieval has emerged. Context-aware music recommender systems are capable of suggesting music items taking into consideration contextual conditions, such as the user's mood or location, that may influence the user's preferences at a particular moment. In this work, we consider a particular kind of context-aware recommendation taskselecting music content that fits a place of interest (POI). To address this problem we have used emotional tags assigned to both music tracks and POIs, and we have considered a set of similarity metrics for tagged resources to establish a match between music tracks and POIs. Following an initial webbased evaluation of the core matching technique, we have developed a mobile application that suggests an itinerary and plays recommended music for each visited POI, and evaluated it in a live user study. The results of the study show that users judge the recommended music as suited for the POIs, and that the music is rated higher when it is played in this usage scenario.

Lecture Notes in Computer Science, 2013

Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossib... more Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the user's personality -using the Five Factor Model (FFM) -in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, contextaware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.

In this demo paper we present a novel context-aware mobile recommender system for places of inter... more In this demo paper we present a novel context-aware mobile recommender system for places of interest (POIs). Unlike existing systems, which learn users' preferences solely from their past ratings, it considers also their personality -using the Five Factor Model. Personality is acquired by asking users to complete a brief and entertaining questionnaire as part of the registration process, and is then exploited in:

In this paper we present STS (South Tyrol Suggests), a context-aware mobile recommender system fo... more In this paper we present STS (South Tyrol Suggests), a context-aware mobile recommender system for places of interest (POIs) that integrates some innovative components, including: a personality questionnaire, i.e., a brief and entertaining questionnaire used by the system to learn users' personality; an active learning module that acquires ratings-in-context for POIs that users are likely to have experienced; and a matrix factorization based recommendation module that leverages the personality information and several contextual factors in order to generate more relevant recommendations. Adopting a system oriented perspective, we describe the assessment of the combination of the implemented components. We focus on usability aspects and report the end-user assessment of STS. It was obtained from a controlled live user study as well as from the log data produced by a larger sample of users that have freely downloaded and tried STS through Google Play Store. The result of the assessment showed that the overall usability of the system falls between "good" and "excellent", it helped us to identify potential problems and it provided valuable indications for future system improvement.

Nowadays, Recommender Systems (RSs) play a key role in many businesses. They provide consumers wi... more Nowadays, Recommender Systems (RSs) play a key role in many businesses. They provide consumers with relevant recommendations, e.g., Places of Interest (POIs) to a tourist, based on user preference data, mainly in the form of ratings for items. The accuracy of recommendations largely depends on the quality and quantity of the ratings (preferences) provided by the users. However, users often tend to rate no or only few items, causing low accuracy of the recommendation. Active Learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire a larger number of high-quality ratings (preferences), and hence, improve the recommendation accuracy. In this paper, we propose a personalized active learning approach that leverages user's personality data to get more and better in-context ratings. We have designed a novel human computer interaction and assessed our proposed approach in a live user study -which is not common in active learning research. The main result is that the system is able to collect better ratings and provide more relevant recommendations compared to a variant that is using a state of the art approach to preference acquisition.