Ashutosh Garg

Google news personalization: scalable online collaborative filtering

Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several millionusers and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and consequently domain independent, making it easily adaptable for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News.

A discussion of nonlinear variants of biased discriminants for interactive image retrieval

During an interactive image retrieval process with relevance feedback, kernel-based or boosted learning algorithms can provide superior nonlinear modeling capability. In this paper, we discuss such nonlinear extensions for biased discriminants, or BiasMap [1, 2]. Kernel partial alignment is proposed as the criterion for kernel selection. The associated analysis also provides a gauge on relative class scatters, which can guide an asymmetric learner, such as BiasMap, toward better class modeling. We also propose two boosted versions of BiasMap. Unlike existing approach that boosts feature components or vectors to form a composite classifier, our scheme boosts linear BiasMap toward a nonlinear ranker which is more suited for small-sample learning during interactive image retrieval. Experiments on heterogeneous image database retrieval as well as small sample face retrieval are used for performance evaluations.