Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction

By Bin Li, Qiang Yang, Xiangyang Xue. 2009

457 citations recorded by the source corpus.

How researchers see this paper

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

1. Qiang Yang
2. Weike Pan
3. Yu Zhang
4. Qian Zhang
5. Xiangnan He
6. Chen Gao
7. Fuzhen Zhuang
8. Guangneng Hu

Citations in the corpus

• Transfer Learning in Collaborative Filtering with Uncertain Ratings (2012, methodology): “A new direction is to apply transfer learning to collaborative filtering (Li, Yang, and Xue 2009b; Pan et al. 2011b), so that one can make use of auxiliary data to help improve the rating prediction performance.”
• Transfer Learning in Collaborative Filtering for Sparsity Reduction (2010, unknown): “Also, CBT and RMGM can neither make use of user side nor item side shared information, and only take a general explicit rating matrix as its auxiliary input.”
• Cross-Domain Recommendation: An Embedding and Mapping Approach (2017, background): “The first type works in an asymmetric way, aiming to leverage data in an auxiliary domain to alleviate data sparsity of the target domain [Li et al., 2009a; 2009b; Pan et al., 2010].”
• Feature-based transfer learning with real-world applications (2010, background): “Web applications of transfer learning include text classification across domains [151, 47, 48, 72, 195, 207, 36, 204, 213, 203], advertising [40, 39], learn to rank across domains [10, 192, 35, 68] and multi-domain collaborative filtering [103, 102, 29, 223, 143].”
• Artificial intelligence in recommender systems (2020, background): “Users and items are clusteredintogroupsandknowledgeissharedthroughgroup-level rating patterns; for example, codebook transfer (CBT) clusters users and items into groups and extracts group-level knowledge as a “codebook” [115].”
• Cross-Domain Recommendation via Cluster-Level Latent Factor Model (2013, methodology): “Following the work in [4], we examine the compared models for the cross-domain recommendation task.”
• Nonparametric bayesian multitask collaborative filtering (2013, unknown): “, [18, 23, 20, 16]).”
• Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text (2019, background): “neighborhood who consume the common items with this user [11]. Another solution is to transfer the knowledge from relevant domains and the cross-domain recommendation techniques address such problems [3, 27, 39]. In real life, a user typically participates several systems to acquire different inform”
• Protein-protein interaction prediction via Collective Matrix Factorization (2010, methodology): “To solve the problem, various matrix based transfer learning methods have been developed [22], [23], [24].”
• Cross-Domain Recommender Systems (2015, methodology): “…al. 2014 [44] books, games, music, movies & TV shows ratings Winoto & Tang 2008 [66] item books, movies ratings - BookCrossing, MovieLens/EachMovie Li et al. 2009 [40] [41] Gao et al. 2013 [26] ratings, tags - LibraryThing, MovieLens Zhang et al. 2010 [67] Shi et al. 2011 [59] Enrich et al. 2013…”
• Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “Codebook transfer (CBT) [Li et al. 2009a] and the rating-matrix generative model (RMGM) [Li et al.”
• Cross-domain Recommendation Without Sharing User-relevant Data (2019, methodology): “[22] proposed a model named CBT (Code Book Transfer) which builds a matrix named codebook to represent cluster-level rating pattern and this codebook is shared by two domains.”
• Transfer learning in heterogeneous collaborative filtering domains (2013, methodology): “In the target data, we have a user-item numerical rating matrix R = [rui]n×m ∈ {1,2,3,4,5,?}n×m with q observed ratings, where the question mark “?” denotes a missing value, which can be an unobserved value.”