Abstract: In this paper, we propose a novel spatio-temporal model for
collaborative filtering applications. Our model is based on
low-rank matrix factorization that uses a spatio-temporal
filtering approach to estimate user and item factors. The
spatial component regularizes the factors by exploiting correlation
across users and/or items, modeled as a function
of some implicit feedback (e.g., who rated what) and/or
some side information (e.g., user demographics, browsing
history). In particular, we incorporate correlation in factors
through a Markov random field prior in a probabilistic
framework, whereby the neighborhood weights are functions
of user and item covariates. The temporal component ensures
that the user/item factors adapt to process changes
that occur through time and is implemented in a state space
framework with fast estimation through Kalman filtering.
Our spatio-temporal filtering (ST-KF hereafter) approach
provides a single joint model to simultaneously incorporate
both spatial and temporal structure in ratings and therefore
provides an accurate method to predict future ratings. To
ensure scalability of ST-KF, we employ a mean-field approximation
for inference. Incorporating user/item covariates in
estimating neighborhood weights also helps in dealing with
both cold-start and warm-start problems seamlessly in a single
unified modeling framework; covariates predict factors
for new users and items through the neighborhood. We illustrate
our method on simulated data, benchmark data and
data obtained from a relatively new recommender system
application arising in the context of Yahoo! Front Page.
- Topics:
- Recommender Systems
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Citation
- A Spatio-Temporal Approach to Collaborative Filtering (pdf, software)
Z. Lu, D. Agarwal, I. Dhillon.
In ACM Conference on Recommender Systems (RecSys), October 2009.
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