SVDFeature: a toolkit for feature-based collaborative filtering

By Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu. 2012

273 citations recorded by the source corpus.

Subfields: Information Retrieval & Search

How researchers see this paper

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

In Information Retrieval & Search

1. Philip S. Yu
2. Xing Xie
3. Chuan Shi
4. Yue Shi
5. Ting-Li Chen
6. Binbin Hu
7. Yizhou Sun
8. Lichan Hong

Citations in the corpus

• On Sampling Strategies for Neural Network-based Collaborative Filtering (2017, methodology): “To address the issue and improve performance, hybrid methods are proposed to incorporate side information [5, 7, 25, 28, 38], as well as content information [4, 11, 31, 32].”
• Collaborative Deep Learning for Recommender Systems (2014, background): “• SVDFeature: SVDFeature [8] is a model for featurebased collaborative filtering.”
• Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, neutral): “SVDfeature, libFM) have been developed [9, 31].”
• Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention (2017, unknown): “Experimental results show that ACF consistently outperforms competing methods ranging from CF-based methods, content-based methods [28, 35] and hybrid methods [9, 33] (cf. Section 5).”
• Attention-driven Factor Model for Explainable Personalized Recommendation (2018, background): “- SVDFeature [3].”
• Heterogeneous Information Network Embedding for Recommendation (2017, methodology): “[29] propose a typical SVDFeature framework to efficiently solve the feature based matrix factorization.”
• Sampling-bias-corrected neural modeling for large corpus item recommendations (2019, methodology): “For instance, the generalized matrix factorization models, e.g., SVDFeature [8] and Factorization Machine [33], can be applied to incorporate item content features.”
• Joint Text Embedding for Personalized Content-based Recommendation (2017, methodology): “On the other hand, for collaborative filtering methods, although some of which has been extended to incorporate auxiliary information, text feature extraction functions are usually simple, and cannot leverage recent proposed representation learning techniques for text [5, 22, 27].”
• Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks (2016, methodology): “For CMF and SVDFeature, optimal regularization hyperparameters are used for different P .”
• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (2018, background): “[3, 27] extend MF to leveraging side information, in which both a linear model and a MF model are included.”
• 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).”
• Learning Graph-based POI Embedding for Location-based Recommendation (2016, methodology): “SVDFeature [2] is a machine learning toolkit designed to solve the feature-based matrix factorization.”
• Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model (2018, methodology): “(3) Among the two kinds of baselines, most of HIN based methods (SVDFeaturehete , SVDFeaturemp and FMGrank ) outperform CF methods (ItemKNN, BPR and MF) in most cases, which indicates the usefulness of heterogeneous information.”