4 research outputs found
Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation
Cross-platform recommendation aims to improve recommendation accuracy through
associating information from different platforms. Existing cross-platform
recommendation approaches assume all cross-platform information to be
consistent with each other and can be aligned. However, there remain two
unsolved challenges: i) there exist inconsistencies in cross-platform
association due to platform-specific disparity, and ii) data from distinct
platforms may have different semantic granularities. In this paper, we propose
a cross-platform association model for cross-platform video recommendation,
i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking
platform-specific disparity and granularity difference into consideration. The
proposed DCA model employs a partially-connected multi-modal autoencoder, which
is capable of explicitly capturing platform-specific information, as well as
utilizing nonlinear mapping functions to handle granularity differences. We
then present a cross-platform video recommendation approach based on the
proposed DCA model. Extensive experiments for our cross-platform recommendation
framework on real-world dataset demonstrate that the proposed DCA model
significantly outperform existing cross-platform recommendation methods in
terms of various evaluation metrics
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
Traditional recommendation systems are faced with two long-standing
obstacles, namely, data sparsity and cold-start problems, which promote the
emergence and development of Cross-Domain Recommendation (CDR). The core idea
of CDR is to leverage information collected from other domains to alleviate the
two problems in one domain. Over the last decade, many efforts have been
engaged for cross-domain recommendation. Recently, with the development of deep
learning and neural networks, a large number of methods have emerged. However,
there is a limited number of systematic surveys on CDR, especially regarding
the latest proposed methods as well as the recommendation scenarios and
recommendation tasks they address. In this survey paper, we first proposed a
two-level taxonomy of cross-domain recommendation which classifies different
recommendation scenarios and recommendation tasks. We then introduce and
summarize existing cross-domain recommendation approaches under different
recommendation scenarios in a structured manner. We also organize datasets
commonly used. We conclude this survey by providing several potential research
directions about this field