2010 FastOnlineLearningthroughOfflin

• (Agarwal et al., 2010) ⇒ Deepak Agarwal, Bee-Chung Chen, and Pradheep Elango. (2010). “Fast Online Learning through Offline Initialization for Time-sensitive Recommendation.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835894

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Cited By

• http://scholar.google.com/scholar?q=%22Fast+online+learning+through+offline+initialization+for+time-sensitive+recommendation%22+2010
• http://portal.acm.org/citation.cfm?id=1835894&preflayout=flat#citedby

Quotes

Author Keywords

Reduced rank regression, recommender systems, latent factor, factorization, dyadic data

Abstract

Recommender problems with large and dynamic item pools are ubiquitous in web applications like content optimization, online advertising and web search. Despite the availability of rich item meta-data, excess heterogeneity at the item level often requires inclusion of item-specific "factors" (or weights) in the model. However, since estimating item factors is computationally intensive, it poses a challenge for time-sensitive recommender problems where it is important to rapidly learn factors for new items (e.g., news articles, event updates, tweets) in an online fashion. In this paper, we propose a novel method called FOBFM (Fast Online Bilinear Factor Model) to learn item-specific factors quickly through online regression. The online regression for each item can be performed independently and hence the procedure is fast, scalable and easily parallelizable. However, the convergence of these independent regressions can be slow due to high dimensionality. The central idea of our approach is to use a large amount of historical data to initialize the online models based on offline features and learn linear projections that can effectively reduce the dimensionality. We estimate the rank of our linear projections by taking recourse to online model selection based on optimizing predictive likelihood. Through extensive experiments, we show that our method significantly and uniformly outperforms other competitive methods and obtains relative lifts that are in the range of 10-15% in terms of predictive log-likelihood, 200-300% for a rank correlation metric on a proprietary My Yahoo! dataset; it obtains 9% reduction in root mean squared error over the previously best method on a benchmark MovieLens dataset using a time-based train/test data split.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 FastOnlineLearningthroughOfflin	Bee-Chung Chen Deepak Agarwal Pradheep Elango			Fast Online Learning through Offline Initialization for Time-sensitive Recommendation		KDD-2010 Proceedings		10.1145/1835804.1835894		2010