Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence

By Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei. 2016

277 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Qiang Yang
2. Yongfeng Zhang
3. Philip S. Yu
4. David M. Blei
5. Xing Xie
6. Wayne Xin Zhao
7. Qingyao Ai
8. Yu Zhang

In Information Systems

1. Yongfeng Zhang
2. Xiangnan He
3. Xing Xie
4. Qingyao Ai
5. Philip S. Yu
6. Xu Chen
7. Chuan Shi
8. Qiang Yang

In Recommender Systems

1. Yongfeng Zhang
2. Xing Xie
3. Fuzheng Zhang
4. Qingyao Ai
5. Susan Athey
6. Dawen Liang
7. Philip S. Yu
8. Wayne Xin Zhao

Citations in the corpus

• Variational Autoencoders for Collaborative Filtering (2018, background): “ly dominate the collaborative filtering research literature due to their simplicity and effectiveness. However, these models are inherently linear, which limits their modeling capacity. Previous work [27] has demonstrated that adding carefully crafted non-linear features into the linear latent facto”
• SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements (2017, background): “There has been some work on extrapolating embedding ideas to shopping items (Rudolph et al., 2016; Liang et al., 2016; Barkan and Koenigstein, 2016), but these newer methods are not readily appropriate to the analyses here.”
• Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback (2019, unknown): “tems to predict and provide users with items that they may be interested in to improve the user experiences. To provide such recommendations, predicting the items that users like is an important task [6,8,14]. Generally, one can use two types of data for the recommendation. One includes users’ ratin”
• A Survey on Session-based Recommender Systems (2019, methodology): “A co-factorization model, CoFactor, was proposed to jointly decompose the user-item interaction matrix and the item-item co-occurrence matrix with shared latent factors for items to capture both the users’ individual preference and the item transition patterns [56].”
• Learning Tree-based Deep Model for Recommender Systems (2018, methodology): “To evaluate the effectiveness of different methods, we use Precision@M, Recall@M and F-Measure@M metrics [16].”
• Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text (2019, methodology): “To address the data sparsity, an item-item matrix (SPPM) is constructed from the user-item interaction matrix in the CoFactor model [28].”
• Heterogeneous Information Network Embedding for Recommendation (2017, methodology): “On the other hand, network embedding has shown its potential in structure feature extraction and has been successfully applied in many data mining tasks [39], [40], [41], [42], such as classification [43], clustering [44], [45] and recommendation [46], [47].”
• Sequential Recommender System based on Hierarchical Attention Networks (2018, methodology): “[Liang et al. , 2016] utilized word embedding to extract information from item-item co-occurrence to improve matrix factorization performance.”
• Explainable Product Search with a Dynamic Relation Embedding Model (2019, methodology): “[34] propose to jointly decompose the user-item interaction matrix and the item-item co-occurrence matrix with shared item embeddings.”