4 research outputs found
Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
Recommendation from implicit feedback is a highly challenging task due to the
lack of the reliable observed negative data. A popular and effective approach
for implicit recommendation is to treat unobserved data as negative but
downweight their confidence. Naturally, how to assign confidence weights and
how to handle the large number of the unobserved data are two key problems for
implicit recommendation models. However, existing methods either pursuit fast
learning by manually assigning simple confidence weights, which lacks
flexibility and may create empirical bias in evaluating user's preference; or
adaptively infer personalized confidence weights but suffer from low
efficiency. To achieve both adaptive weights assignment and efficient model
learning, we propose a fast adaptively weighted matrix factorization (FAWMF)
based on variational auto-encoder. The personalized data confidence weights are
adaptively assigned with a parameterized neural network (function) and the
network can be inferred from the data. Further, to support fast and stable
learning of FAWMF, a new specific batch-based learning algorithm fBGD has been
developed, which trains on all feedback data but its complexity is linear to
the number of observed data. Extensive experiments on real-world datasets
demonstrate the superiority of the proposed FAWMF and its learning algorithm
fBGD
Towards Responsible Media Recommendation
Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as recommendations is nowadays often automatically determined by AI algorithms, typically with the goal of helping consumers discover relevant content more easily. However, the highlighting or filtering of information that comes with such recommendations may lead to undesired effects on consumers or even society, for example, when an algorithm leads to the creation of filter bubbles or amplifies the spread of misinformation. These well-documented phenomena create a need for improved mechanisms for responsible media recommendation, which avoid such negative effects of recommender systems. In this research note, we review the threats and challenges that may result from the use of automated media recommendation technology, and we outline possible steps to mitigate such undesired societal effects in the future.publishedVersio