Matrix Factorization

Adams, Roy J., Rajani S. Sadasivam, Kavitha Balakrishnan, Rebecca L. Kinney, Thomas K. Houston, and Benjamin M. Marlin. "PERSPeCT: Collaborative Filtering for Tailored Health Communications." Proceedings of the 8th ACM Conference on Recommender Systems. RecSys '14. New York, NY, USA: ACM, 2014. 329-332. Abstractperspect-recsys14.pdf

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The goal of computer tailored health communications (CTHC) is to elicit healthy behavior changes by sending motivational messages personalized to individual patients. One prominent weakness of many existing CTHC systems is that they are based on expert-written rules and thus have no ability to learn from their users over time. One solution to this problem is to develop CTHC systems based on the principles of collaborative filtering, but this approach has not been widely studied. In this paper, we present a case study evaluating nine rating prediction methods for use in the Patient Experience Recommender System for Persuasive Communication Tailoring, a system developed for use in a clinical trial of CTHC-based smoking cessation support interventions.

Riedel, Sebastian, Limin Yao, Andrew McCallum, and Benjamin M. Marlin. "Relation Extraction with Matrix Factorization and Universal Schemas." HLT-NAACL. 2013. 74-84. Abstractuniv-schema_naacl13_paper.pdf

Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing struc- tured sources of the same schema. The need for existing datasets can be avoided by using a universal schema: the union of all in- volved schemas (surface form predicates as in OpenIE, and relations in the schemas of pre- existing databases). This schema has an al- most unlimited set of relations (due to surface forms), and supports integration with existing structured data (through the relation types of existing databases). To populate a database of such schema we present matrix factorization models that learn latent feature vectors for en- tity tuples and relations. We show that such latent models achieve substantially higher accuracy than a traditional classification approach. More importantly, by operating simultaneously on relations observed in text and in pre-existing structured DBs such as Freebase, we are able to reason about unstructured and structured data in mutually-supporting ways. By doing so our approach outperforms state-of-the-art distant supervision.

Marlin, Benjamin M., Roy J. Adams, Rajani Sadasivam, and Thomas K. Houston Towards Collaborative Filtering Recommender Systems for Tailored Health Communications. AMIA 2013 Annual Symposium., 2013. Abstractcthc_recsys13_paper.pdf

The goal of computer tailored health communications (CTHC) is to promote healthy behaviors by sending messages tailored to individual patients. Current CTHC systems collect baseline patient “profiles” and then use expert-written, rule-based systems to target messages to subsets of patients. Our main interest in this work is the study of collaborative filtering-based CTHC systems that can learn to tailor future message selections to individual patients based explicit feedback about past message selections. This paper reports the results of a study designed to collect explicit feedback (ratings) regarding four aspects of messages from 100 subjects in the smoking cessation support domain. Our results show that most users have positive opinions of most messages and that the ratings for all four aspects of the messages are highly correlated with each other. Finally, we conduct a range of rating prediction experiments comparing several different model variations. Our results show that predicting future ratings based on each user’s past ratings contributes the most to predictive accuracy.