3,660 research outputs found
Sparse online collaborative filtering with dynamic regularization
Abstract(#br)Collaborative filtering (CF) approaches are widely applied in recommender systems. Traditional CF approaches have high costs to train the models and cannot capture changes in user interests and item popularity. Most CF approaches assume that user interests remain unchanged throughout the whole process. However, user preferences are always evolving and the popularity of items is always changing. Additionally, in a sparse matrix, the amount of known rating data is very small. In this paper, we propose a method of online collaborative filtering with dynamic regularization (OCF-DR), that considers dynamic information and uses the neighborhood factor to track the dynamic change in online collaborative filtering (OCF). The results from experiments on the MovieLens100K, MovieLens1M, and HetRec2011 datasets show that the proposed methods are significant improvements over several baseline approaches
Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition
We present a natural generalization of the recent low rank + sparse matrix
decomposition and consider the decomposition of matrices into components of
multiple scales. Such decomposition is well motivated in practice as data
matrices often exhibit local correlations in multiple scales. Concretely, we
propose a multi-scale low rank modeling that represents a data matrix as a sum
of block-wise low rank matrices with increasing scales of block sizes. We then
consider the inverse problem of decomposing the data matrix into its
multi-scale low rank components and approach the problem via a convex
formulation. Theoretically, we show that under various incoherence conditions,
the convex program recovers the multi-scale low rank components \revised{either
exactly or approximately}. Practically, we provide guidance on selecting the
regularization parameters and incorporate cycle spinning to reduce blocking
artifacts. Experimentally, we show that the multi-scale low rank decomposition
provides a more intuitive decomposition than conventional low rank methods and
demonstrate its effectiveness in four applications, including illumination
normalization for face images, motion separation for surveillance videos,
multi-scale modeling of the dynamic contrast enhanced magnetic resonance
imaging and collaborative filtering exploiting age information
Dynamic Matrix Factorization with Priors on Unknown Values
Advanced and effective collaborative filtering methods based on explicit
feedback assume that unknown ratings do not follow the same model as the
observed ones (\emph{not missing at random}). In this work, we build on this
assumption, and introduce a novel dynamic matrix factorization framework that
allows to set an explicit prior on unknown values. When new ratings, users, or
items enter the system, we can update the factorization in time independent of
the size of data (number of users, items and ratings). Hence, we can quickly
recommend items even to very recent users. We test our methods on three large
datasets, including two very sparse ones, in static and dynamic conditions. In
each case, we outrank state-of-the-art matrix factorization methods that do not
use a prior on unknown ratings.Comment: in the Proceedings of 21st ACM SIGKDD Conference on Knowledge
Discovery and Data Mining 201
Collaborative Deep Learning for Recommender Systems
Collaborative filtering (CF) is a successful approach commonly used by many
recommender systems. Conventional CF-based methods use the ratings given to
items by users as the sole source of information for learning to make
recommendation. However, the ratings are often very sparse in many
applications, causing CF-based methods to degrade significantly in their
recommendation performance. To address this sparsity problem, auxiliary
information such as item content information may be utilized. Collaborative
topic regression (CTR) is an appealing recent method taking this approach which
tightly couples the two components that learn from two different sources of
information. Nevertheless, the latent representation learned by CTR may not be
very effective when the auxiliary information is very sparse. To address this
problem, we generalize recent advances in deep learning from i.i.d. input to
non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian
model called collaborative deep learning (CDL), which jointly performs deep
representation learning for the content information and collaborative filtering
for the ratings (feedback) matrix. Extensive experiments on three real-world
datasets from different domains show that CDL can significantly advance the
state of the art
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