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Advances in collaborative filtering
Collaborative filtering (CF) methods produce recommendations based on usage patterns
without the need of exogenous information about items or users. CF algorithms have shown …
without the need of exogenous information about items or users. CF algorithms have shown …
Neural collaborative filtering vs. matrix factorization revisited
Embedding based models have been the state of the art in collaborative filtering for over a
decade. Traditionally, the dot product or higher order equivalents have been used to …
decade. Traditionally, the dot product or higher order equivalents have been used to …
How powerful is graph convolution for recommendation?
Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms
for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical …
for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical …
On the difficulty of evaluating baselines: A study on recommender systems
Numerical evaluations with comparisons to baselines play a central role when judging
research in recommender systems. In this paper, we show that running baselines properly is …
research in recommender systems. In this paper, we show that running baselines properly is …
Parameter-free dynamic graph embedding for link prediction
Dynamic interaction graphs have been widely adopted to model the evolution of user-item
interactions over time. There are two crucial factors when modelling user preferences for link …
interactions over time. There are two crucial factors when modelling user preferences for link …
Modeling dynamic missingness of implicit feedback for recommendation
Implicit feedback is widely used in collaborative filtering methods for recommendation. It is
well known that implicit feedback contains a large number of values that are\emph {missing …
well known that implicit feedback contains a large number of values that are\emph {missing …
Learning with noisy labels by efficient transition matrix estimation to combat label miscorrection
Recent studies on learning with noisy labels have shown remarkable performance by
exploiting a small clean dataset. In particular, model agnostic meta-learning-based label …
exploiting a small clean dataset. In particular, model agnostic meta-learning-based label …
Learning hierarchical preferences for recommendation with mixture intention neural stochastic processes
User preferences behind users' decision-making processes are highly diverse and may
range from lower-level concepts with more specific intentions and higher-level concepts with …
range from lower-level concepts with more specific intentions and higher-level concepts with …
Learning self-modulating attention in continuous time space with applications to sequential recommendation
User interests are usually dynamic in the real world, which poses both theoretical and
practical challenges for learning accurate preferences from rich behavior data. Among …
practical challenges for learning accurate preferences from rich behavior data. Among …
Triple structural information modelling for accurate, explainable and interactive recommendation
In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns,
represented by different structural information, such as user-item co-occurrence, sequential …
represented by different structural information, such as user-item co-occurrence, sequential …