Personalized recommendation via cross-domain triadic factorization

By Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, Can Zhu. 2013

273 citations recorded by the source corpus.

Subfields: Information Systems, Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Dit-Yan Yeung
2. Qiang Yang
3. Hao Wang
4. Yu Zhang
5. Hongwei Wang
6. Miao Zhao
7. Weike Pan
8. Jie Lu

In Recommender Systems

1. Dit-Yan Yeung
2. Hao Wang
3. Miao Zhao
4. Hongwei Wang
5. Naiyan Wang
6. Minyi Guo
7. Weike Pan
8. Zhong Ming

Citations in the corpus

• Collaborative Deep Learning for Recommender Systems (2014, methodology): “Hybrid methods [1, 18, 12] seek to get the best of both worlds by combining content-based and CF-based methods.”
• Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017, methodology): “Hybrid methods [9, 16] combine content-based and collaborative filtering models in many hybridization approaches, such as weighted, switching, cascade and feature combination or augmentation.”
• Cross-domain Recommendation Without Sharing User-relevant Data (2019, unknown): “The task of leveraging auxiliary data from other domains to improve recommendation quality of the target domain is known as the cross-domain recommendation [1, 10, 15, 32, 35, 43].”
• Deep Collaborative Filtering via Marginalized Denoising Auto-encoder (2015, methodology): “Recently, MF based collaborative filtering is also applied to response prediction [30, 31].”
• Cross-Domain Recommendation: Challenges, Progress, and Prospects (2021, background): “For example, some of them treated items (e.g., movies and books) as different domains [ Hu et al. , 2013 ] , while others considered items in different sub-categories, e.g., such as textbooks and novels, as different domains [ Cao et al. , 2010 ] .”
• Temporal QoS-aware web service recommendation via non-negative tensor factorization (2014, methodology): “Tensor factorization is an important technique in many applications, such as data mining, dimensionality reduction, chemometrics, signal processing, neuroscience, and web analysis [7, 10, 26, 8].”
• Cross-Domain Recommender Systems (2015, methodology): “…Enrich et al. 2013 [20] N Fernández-Tobías & Cantador 2014 [24] N Sharing latent features via a user-item-domain tensor factorization Rating overlap Hu et al. 2013 [31] U (N) no overlap, (U) user overlap, (I) item overlap, (UI) user and item overlap. ture the heterogeneity of items, and that…”
• Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation (2015, methodology): “Illustration of compressed knowledge transfer (CKT) via factorization and auxiliary data, which can thus be categorized as a frontal-side transfer learning setting [23], rather than the two-side [22], user-side [7] or item-side [29] knowledge transfer setting.”
• CityTransfer (2018, background): “While traditional CF cannot be directly used to solve the above two-fold knowledge transfer problem, it has been proved easily-extensible to address complicated recommendation problems [17,22].”
• CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (2018, methodology): “It showed better performance than other cross-domain methods like triadic (tensor) factorization [16].”
• Collaborative filtering and deep learning based recommendation system for cold start items (2017, methodology): “A The hybrid approach is one that combines CB filtering approach and CF approach attempting to overcome their shortcome and provide a more efficient result (Agarwal, & Chen, 2009; Chen et al., 2012; Hu et al., 2013).”
• Artificial intelligence in recommender systems (2020, background): “Cross-domain triadic factorization (CDTF) models a user-item-domain tensor to integrate both explicit and implicit user feedback [120].”