48,622 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
Improving the quality of the personalized electronic program guide
As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems—PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system
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