A Probabilistic View of Neighborhood-based Recommendation Methods
This work addresses recommendation accuracy for users, but it appears incremental as it builds on existing neighborhood-based methods with a probabilistic twist.
The paper tackles the problem of improving neighborhood-based recommendation methods by proposing a probabilistic framework (PNBM) that treats similarity as an unobserved factor and maximizes a posterior over it, with results showing very accurate estimation of user preferences on real-world datasets.
Probabilistic graphic model is an elegant framework to compactly present complex real-world observations by modeling uncertainty and logical flow (conditionally independent factors). In this paper, we present a probabilistic framework of neighborhood-based recommendation methods (PNBM) in which similarity is regarded as an unobserved factor. Thus, PNBM leads the estimation of user preference to maximizing a posterior over similarity. We further introduce a novel multi-layer similarity descriptor which models and learns the joint influence of various features under PNBM, and name the new framework MPNBM. Empirical results on real-world datasets show that MPNBM allows very accurate estimation of user preferences.