Peter Forbes
ABSTRACT
The Netflix prize has rejuvenated a widespread interest in the matrix factorization approach for collaborative filtering. We describe a simple algorithm for incorporating content information directly into this approach. We present experimental evidence using recipe data to show that this not only improves recommendation accuracy but also provides useful insights about the contents themselves that are otherwise unavailable.
- Freyne, J. and Berkovsky, S. (2010). Intelligent food planning: Personalized recipe recommendation. In Proceedings of the 15th International Conference on Intelligent User Interfaces, pages 321--324. Google Scholar
Digital Library
- Koren, Y. (2008). Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 426--434. Google ScholarDigital Library
- 009)}netflixKoren, Y., Bell, R., and Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30--37. Google ScholarDigital Library
- Melville, P., Mooney, R. J., and Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendation. In Proceedings of the 18th National Conference on Artificial Intelligence, pages 187--192. Google ScholarDigital Library
- Su, X. and Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009. Article ID 421425. Google ScholarDigital Library
- ter Braak, C. J. F. (1986). Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology, 67(5), 1167--1179.Google ScholarCross Ref
- van Pinxteren, Y., Geleijnse, G., and Kamsteeg, P. (2011). Deriving a recipe similarity measure for recommending healthful meals. In Proceedings of the 16th International Conference on Intelligent User Interfaces, pages 105--114. Google ScholarDigital Library
Index Terms
- Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation
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