Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Using Sequential and Non-Sequential Patterns in Predictive Web Usage Mining Tasks
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The Journal of Machine Learning Research
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Location-adapted music recommendation using tags
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
A context-aware music recommendation system using fuzzy bayesian networks with utility theory
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Exploring generative models of tripartite graphs for recommendation in social media
Proceedings of the 4th International Workshop on Modeling Social Media
Goal attainment on long tail web sites: An information foraging approach
Decision Support Systems
Which app will you use next?: collaborative filtering with interactional context
Proceedings of the 7th ACM conference on Recommender systems
Query-driven context aware recommendation
Proceedings of the 7th ACM conference on Recommender systems
Escape the bubble: guided exploration of music preferences for serendipity and novelty
Proceedings of the 7th ACM conference on Recommender systems
Social Link Prediction in Online Social Tagging Systems
ACM Transactions on Information Systems (TOIS)
Comparing context-aware recommender systems in terms of accuracy and diversity
User Modeling and User-Adapted Interaction
| Hi-index | 0.00 |
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.