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LKT-FM: A Novel Rating Pattern Transfer Model for Improving Non-overlapping Cross-Domain Collaborative Filtering
Conference proceeding   Peer reviewed

LKT-FM: A Novel Rating Pattern Transfer Model for Improving Non-overlapping Cross-Domain Collaborative Filtering

Yizhou Zang and Xiaohua Hu
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2017, PT II, v 10535, pp 641-656
01 Jan 2017
DOI: https://doi.org/10.1007/978-3-319-71246-8_39

Abstract

Computer Science Computer Science, Artificial Intelligence Computer Science, Information Systems Computer Science, Theory & Methods Science & Technology Technology
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.

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5 citations in Web of Science
7 citations in Scopus

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Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods
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