Cross-Domain Recommendation via Tag Matrix Transfer

Fang, Zhou; Gao, Sheng; Li, Bo; Li, Juncen; Liao, Jianxin

doi:10.1109/icdmw.2015.133

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…Song (Song et al 2017) proposed a joint tensor factorization model to fully exploit the aspect factors extracted from reviews. Fang (Fang et al 2015) proposed a novel way to exploit rating patterns across multiple domains by transferring the tag cooccurrence matrix information, which could be used for revealing common user pattern. All these works merely consider one kind of side information and cannot effectively and deeply fuse different side information with ratings.…”

Section: Related Workmentioning

confidence: 99%

Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

Peng

Wang

et al. 2019

AAAI

130

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As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. Although the side information of review texts and item contents has been proven to be useful in recommendation, most existing works only use one kind of side information and cannot deeply fuse this side information with ratings. In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for crossdomain recommendation. We first extend Stacked Denoising Autoencoders (SDAE) to effectively fuse review texts and item contents with the rating matrix in both auxiliary and target domains. Through this way, the learned latent factors of users and items in both domains preserve more semantic information for recommendation. Then we utilize a multi-layer perceptron to transfer user latent factors between the two domains to address the data sparsity and cold start issues. Experimental results on real datasets demonstrate the superior performance of RC-DFM compared with state-of-the-art recommendation methods.Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

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Section: Related Workmentioning

confidence: 99%

Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

Peng

Wang

et al. 2019

AAAI

130

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“…Specifically, the orthogonal nonnegative matrix tri-factorization (ONMTF) algorithm is a widely used co-clustering algorithm that is proved to be equivalent to a two-way K-means clustering algorithm. UserItemTags [28] EC-Web 2013 intra-domain single-target TagGSVD++ [30] RecSys 2014 intra-domain single-target TagCDCF [83] UMAP 2011 intra-domain multi-target ETagiCDCF [35] FUZZ-IEEE 2016 intra-domain multi-target SCT [98] IJCNN 2019 intra-domain multi-target TMT [29] ICDMW 2015 intra-domain multi-target SCD [48] CIDM 2014 inter-domain single-target GRAPH [96] CIKM 2015 inter-domain single-target Applying Active Learning MMMF 𝑇 𝐿 [106] AAAI 2013 intra-domain single-target RLMF 𝑇 𝐿 , PMF 𝑇 𝐿 [107] Artif. Intell.…”

Section: Extracting Cluster-level Rating Patternsmentioning

confidence: 99%

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Zang¹,

Zhu²,

Liu³

et al. 2021

Preprint

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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.

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“…Li et al [20] proposed a unified framework for cross-domain CF over the site-time coordinate system by sharing group-level rating patterns and imposing user/item dependence across domains. Fang et al [21] proposed to exploit the rating patterns across multiple domains by transferring the tag co-occurrence matrix information. Chen et al [22] proposed a criterion based on empirical prediction error and its variance to better capture the consistency across domains in CF settings.…”

Section: Transfer Learningmentioning

confidence: 99%

ACTL: Adaptive Codebook Transfer Learning for Cross-Domain Recommendation

Zhang

2019

IEEE Access

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Collaborative filtering usually suffers from limited performance due to the data sparsity problem. Transfer learning presents an unprecedented opportunity to alleviate this issue through transfer useful knowledge from an auxiliary domain to a target domain. Cluster-level rating patterns transformation models have been widely used due to the loose restriction which does not assume the source overlaps users and items with the target. However, previous researches have never investigated the relationship between the codebook scale in transfer learning and the prediction accuracy in the target domain. Moreover, all existing rating patterns sharing models fix the codebook scale without considering the data features of the source domain. In this paper, we propose a novel model, namely ACTL, to efficiently and automatically discover the appropriate codebook scale, which balances both the computational cost and prediction accuracy and best matches the size and features of the source domain for the cross-domain recommendation. The extensive experiments on real-world datasets demonstrate that our algorithms get knowledge gain from the large source domain and clearly and solidly outperform the state-of-the-art fixed scale codebook transfer learning methods. INDEX TERMS Transfer learning, collaborative filtering, cross-domain recommendation.

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Cross-Domain Recommendation via Tag Matrix Transfer

Cited by 17 publications

References 7 publications

Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

ACTL: Adaptive Codebook Transfer Learning for Cross-Domain Recommendation

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