Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis

By Yongfeng Zhang, Min Zhang, Yi Zhang, Guokun Lai, Yiqun Liu, Honghui Zhang, Shaoping Ma. 2015

80 citations recorded by the source corpus.

Subfields: Information Systems, Recommender Systems, Signal Processing, Time-Series & Forecasting

How researchers see this paper

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

In Information Systems

1. Xu Chen
2. Yongfeng Zhang
3. Julian McAuley
4. Ruining He
5. Xiangnan He
6. Zheng Qin
7. Philip S. Yu
8. Jiaxi Tang

In Recommender Systems

1. Yongfeng Zhang
2. Xu Chen
3. Ruining He
4. Julian McAuley
5. Xiangnan He
6. Liqiang Nie
7. Xin Zhou
8. Xiang Wang

In Signal Processing

1. Yongfeng Zhang
2. Ruining He
3. Julian McAuley
4. Xu Chen

In Time-Series & Forecasting

1. Ruining He
2. Julian McAuley

Citations in the corpus

• Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, background): “Previous models have considered either visual [12, 14] or temporal data [5, 19, 23, 39] in isolation, though few have modeled both aspects simultaneously as we do here.”
• Explainable Recommendation: A Survey and New Perspectives (2018, background): “Zhao et al. [2015] designed a probabilistic graphical model to integrate sentiment, aspect, and region information in a unified framework for improving the performance as well as the explainability of pointof-interest (POI) recommendation.”
• Collaborative Intent Prediction with Real-Time Contextual Data (2017, unknown): “For example, state-of-the-art recommendation models (Charlin et al. 2015; Koren 2009; Zhang et al. 2015) that capture the evolving of user preferences and item attributes cannot effectively solve the intent monitoring problem.”