zhuty16/economics-in-recommender-systems

Economics in Recommender Systems

Economics in Recommender Systems.

Contributed by Tianyu Zhu.

Utility

1. Utilizing marginal net utility for recommendation in e-commerce. Wang, Jian, and Yi Zhang. SIGIR 2011.

2. Multi-product utility maximization for economic recommendation. Zhao, Qi, et al. WSDM 2017.

Substitutable & Complementary

1. Inferring networks of substitutable and complementary products. McAuley, Julian, Rahul Pandey, and Jure Leskovec. KDD 2015.

2. Image-based recommendations on styles and substitutes. McAuley, Julian, et al. SIGIR 2015.

3. A path-constrained framework for discriminating substitutable and complementary products in e-commerce. Wang, Zihan, et al. WSDM 2017. (Best Student Paper Award)

4. Quality-aware neural complementary item recommendation. Zhang, Yin, et al. RecSys 2018.

Causal Inference

1. Causal Inference for Recommendation. Liang, Dawen, Laurent Charlin, and David M. Blei. UAI 2016.

2. Modeling user exposure in recommendation. Liang, Dawen, et al. WWW 2016.

3. Causal embeddings for recommendation. Bonner, Stephen, and Flavian Vasile. RecSys 2018. (Best Paper Award)

4. The Deconfounded Recommender: A Causal Inference Approach to Recommendation. Wang, Yixin, et al. arXiv 2018.

Debiasing

1. Recommendations as treatments: Debiasing learning and evaluation. Schnabel, Tobias, et al. ICML 2016.

2. Modeling the assimilation-contrast effects in online product rating systems: Debiasing and recommendations. Zhang, Xiaoying, Junzhou Zhao, and John Lui. RecSys 2017. (Best Paper Award)

Algorithmic Confounding

1. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. Chaney, Allison JB, Brandon M. Stewart, and Barbara E. Engelhardt. RecSys 2018.

User Expertise

1. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. McAuley, Julian John, and Jure Leskovec. WWW 2013.

Price Sensitivity

1. Modeling consumer preferences and price sensitivities from large-scale grocery shopping transaction logs. Wan, Mengting, et al. WWW 2017.

Seasonal Factor

1. Daily-aware personalized recommendation based on feature-level time series analysis. Zhang, Yongfeng, et al. WWW 2015.

Item Lifetime

1. Opportunity model for e-commerce recommendation: right product; right time. Wang, Jian, and Yi Zhang. SIGIR 2013.

Loyalty

1. Representing and Recommending Shopping Baskets with Complementarity, Compatibility, and Loyalty. Wan, Mengting, et al. CIKM 2018.