LKT-FM: A Novel Rating Pattern Transfer Model for Improving Non-Overlapping Cross-Domain Collaborative Filtering
Abstract
Cross-Domain Collaborative Filtering (CDCF) has attracted various research works in recent years. However, an important problem setting, i.e., “users and items in source and target domains are totally different”, has not received much attention yet. We coin this problem as Non-Overlapping Cross-Domain Collaborative Filtering (NOCDCF). In order to solve this challenging CDCF task, we propose a novel 3-step rating pattern transfer model, i.e. low-rank knowledge transfer via factorization machines (LKT-FM). Our solution is able to mine high quality knowledge from large and sparse source matrices, and to integrate the knowledge without losing much information contained in the target matrix via exploiting Factorization Machine (FM). Extensive experiments on real world datasets show that the proposed LKT-FM model outperforms the state-of-the-art CDCF solutions.
Cite
Text
Zang and Hu. "LKT-FM: A Novel Rating Pattern Transfer Model for Improving Non-Overlapping Cross-Domain Collaborative Filtering." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71246-8_39Markdown
[Zang and Hu. "LKT-FM: A Novel Rating Pattern Transfer Model for Improving Non-Overlapping Cross-Domain Collaborative Filtering." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/zang2017ecmlpkdd-lktfm/) doi:10.1007/978-3-319-71246-8_39BibTeX
@inproceedings{zang2017ecmlpkdd-lktfm,
title = {{LKT-FM: A Novel Rating Pattern Transfer Model for Improving Non-Overlapping Cross-Domain Collaborative Filtering}},
author = {Zang, Yizhou and Hu, Xiaohua},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {641-656},
doi = {10.1007/978-3-319-71246-8_39},
url = {https://mlanthology.org/ecmlpkdd/2017/zang2017ecmlpkdd-lktfm/}
}