Number of co-authors:9
Number of publications with 3 favourite co-authors:Francesco Ricci:Thomas Schimoler:Negar Hariri:
Robin Burke's 3 most productive colleagues in number of publications:Francesco Ricci:25Bamshad Mobasher:18Alexander Felferni..:12
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Publications by Robin Burke (bibliography)
Hariri, Negar, Mobasher, Bamshad and Burke, Robin (2012): Context-aware music recommendation based on latent topic sequential patterns. In: Proceedings of the 2012 ACM Conference on Recommender Systems 2012. pp. 131-138. Available online
Contextual factors can greatly influence the users' preferences in listening to music. Although it is hard to capture these factors directly, it is possible to see their effects on the sequence of songs liked by the user in his/her current interaction with the system. In this paper, we present a context-aware music recommender system which infers contextual information based on the most recent sequence of songs liked by the user. Our approach mines the top frequent tags for songs from social tagging Web sites and uses topic modeling to determine a set of latent topics for each song, representing different contexts. Using a database of human-compiled playlists, each playlist is mapped into a sequence of topics and frequent sequential patterns are discovered among these topics. These patterns represent frequent sequences of transitions between the latent topics representing contexts. Given a sequence of songs in a user's current interaction, the discovered patterns are used to predict the next topic in the playlist. The predicted topics are then used to post-filter the initial ranking produced by a traditional recommendation algorithm. Our experimental evaluation suggests that our system can help produce better recommendations in comparison to a conventional recommender system based on collaborative or content-based filtering. Furthermore, the topic modeling approach proposed here is also useful in providing better insight into the underlying reasons for song selection and in applications such as playlist construction and context prediction.
© All rights reserved Hariri et al. and/or ACM Press
Guzzi, Francesca, Ricci, Francesco and Burke, Robin (2011): Interactive multi-party critiquing for group recommendation. In: Proceedings of the 2011 ACM Conference on Recommender Systems 2011. pp. 265-268. Available online
Group recommender systems (RS) are used to support groups in making common decisions when considering a set of alternatives. Current approaches generate group recommendations based on the users' individual preferences models. We believe that members of a group can reach an agreement more effectively by exchanging proposals suggested by a conventional RS. We propose to use a critiquing RS that has been shown to be effective in single-user recommendation. In the group recommendation context, critiquing allows each user to get new recommendations similar to the proposals made by the other group members and to communicate the rationale behind their own counter-proposals. We describe a mobile application implementing the proposed approach and its evaluation in a live user experiment.
© All rights reserved Guzzi et al. and/or ACM Press
Gemmell, Jonathan, Schimoler, Thomas, Mobasher, Bamshad and Burke, Robin (2011): Tag-Based Resource Recommendation in Social Annotation Applications. In: Proceedings of the 2011 Conference on User Modeling, Adaptation and Personalization 2011. pp. 111-122. Available online
Social annotation systems enable the organization of online resources with user-defined keywords. The size and complexity of these systems make them excellent platforms for the application of recommender systems, which can provide personalized views of complex information spaces. Many researchers have concentrated on the important problem of tag recommendation. Less attention has been paid to the recommendation of resources in the context of social annotation systems. In this paper, we examine the specific case of tag-based resource recommendation and propose a linear-weighted hybrid for the task. Using six real world datasets, we show that our algorithm is more effective than other more mathematically complex techniques.
© All rights reserved Gemmell et al. and/or their publisher
Burke, Robin (2010): Evaluating the dynamic properties of recommendation algorithms. In: Proceedings of the 2010 ACM Conference on Recommender Systems 2010. pp. 225-228. Available online
Collaborative recommendation algorithms are typically evaluated on a static matrix of user rating data. However, when users experience a recommender system, it is dynamic, constantly evolving as new items and new users arrive. The dynamic properties of collaborative recommendation have become important as prediction algorithms based on the interactions of rating histories have been proposed, and as researchers seek to understand problems of robustness and maintenance in rating databases. This paper proposes a new evaluation method for the dynamic aspects of collaborative algorithms, the "temporal leave-one-out" approach, which can provide insight into both user-specific and system-level evolution of recommendation behavior. As a case study, the methodology is applied to the Influence Limiter algorithm , showing that its robustness to attack comes at a high accuracy cost.
© All rights reserved Burke and/or ACM Press
Felfernig, Alexander and Burke, Robin (2008): Constraint-based recommender systems: technologies and research issues. In: Fensel, Dieter and Werthner, Hannes (eds.) Proceedings of the 10th International Conference on Electronic Commerce - ICEC 2008 August 19-22, 2008, Innsbruck, Austria. p. 3. Available online
Shepitsen, Andriy, Gemmell, Jonathan, Mobasher, Bamshad and Burke, Robin (2008): Personalized recommendation in social tagging systems using hierarchical clustering. In: Proceedings of the 2008 ACM Conference on Recommender Systems 2008. pp. 259-266. Available online
Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.
© All rights reserved Shepitsen et al. and/or ACM Press
Sandvig, J. J., Mobasher, Bamshad and Burke, Robin (2007): Robustness of collaborative recommendation based on association rule mining. In: Proceedings of the 2007 ACM Conference on Recommender Systems 2007. pp. 105-112. Available online
Standard memory-based collaborative filtering algorithms, such as k-nearest neighbor, are quite vulnerable to profile injection attacks. Previous work has shown that some model-based techniques are more robust than k-nn. Model abstraction can inhibit certain aspects of an attack, providing an algorithmic approach to minimizing attack effectiveness. In this paper, we examine the robustness of a recommendation algorithm based on the data mining technique of association rule mining. Our results show that the Apriori algorithm offers large improvement in stability and robustness compared to k-nearest neighbor and other model-based techniques we have studied. Furthermore, our results show that Apriori can achieve comparable recommendation accuracy to k-nn.
© All rights reserved Sandvig et al. and/or ACM Press
Burke, Robin (2001): Salticus: Guided Crawling for Personal Digital Libraries. In: JCDL01: Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital Libraries 2001. pp. 88-89. Available online
In this paper, we describe Salticus, a web crawler that learns from users web browsing activity. Salticus enables users to build a personal digital library by collecting documents and generalizing over the user's choices.
© All rights reserved Burke and/or ACM Press
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