On an equivalence between PLSI and LDA
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
GaP: a factor model for discrete data
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IEEE Transactions on Knowledge and Data Engineering
Shape Topics: A Compact Representation and New Algorithms for 3D Partial Shape Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A hybrid approach to item recommendation in folksonomies
Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Personalized search on the world wide web
The adaptive web
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Recommender systems at the long tail
Proceedings of the fifth ACM conference on Recommender systems
Topic analysis for online reviews with an author-experience-object-topic model
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
A topic-based recommender system for electronic marketplace platforms
Expert Systems with Applications: An International Journal
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One of the major strengths of probabilistic topic modeling is the ability to reveal hidden relations via the analysis of co-occurrence patterns on dyadic observations, such as document-term pairs. However, in many practical settings, the extreme sparsity and volatility of co-occurrence patterns within the data, when the majority of terms appear in a single document, limits the applicability of topic models. In this paper, we propose an efficient topic modeling framework in the presence of volatile dyadic observations when direct topic modeling is infeasible. We show both theoretically and empirically that often-available unstructured and semantically-rich meta-data can serve as a link between dyadic sets, and can allow accurate and efficient inference. Our approach is general and can work with most latent variable models, which rely on stable dyadic data, such as pLSI, LDA, and GaP. Using transactional data from a major e-commerce site, we demonstrate the effectiveness as well as the applicability of our method in a personalized recommendation system for volatile items. Our experiments show that the proposed learning method outperforms the traditional LDA by capturing more persistent relations between dyadic sets of wide and practical significance.