Why I like it: multi-task learning for recommendation and explanation

By Yichao Lu, Ruihai Dong, Barry Smyth. 2018

147 citations recorded by the source corpus.

We describe a novel, multi-task recommendation model, which jointly learns to perform rating prediction and recommendation explanation by combining matrix factorization, for rating prediction, and adversarial sequence to sequence learning for explanation generation. The result is evaluated using real-world datasets to demonstrate improved rating prediction performance, compared to state-of-the-art alternatives, while producing effective, personalized explanations.

Subfields: Artificial Intelligence, Information 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. Jun Zhou
2. Xudong Gong
3. Chaochao Chen
4. Hongyan Tang
5. Feng Zhu
6. Longfei Li
7. Ming Zhao
8. Yan Wang

In Information Systems

1. Chaochao Chen
2. Feng Zhu
3. Jun Zhou
4. Longfei Li
5. Yan Wang
6. Guanfeng Liu

Citations in the corpus

• Cross-Domain Recommendation: Challenges, Progress, and Prospects (2021, background): “We also summarize the popular datasets in CDRs. [Agarwal et al. , 2011] Multiple contexts Multi-task learning [Lu et al. , 2018] Ratings Multi-task learning [Li et al. , 2009b] Ratings Transfer learning [Pan et al. , 2010] Heterogeneous feedback Principle coordinates [Zhao et al. , 2013] Ratings…”
• Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations (2020, background): “Some studies integrate traditional recommendation algorithms such as collaborative filtering and matrix factorization with MTL. Lu et al. [11] and Wang et al. [23] impose regularization on latent representations learned for the recommendation task and explanation task to optimize them jointly.”