Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation

By Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, Jiawei Han. Knowledge Discovery and Data Mining, 2017

316 citations recorded by the source corpus.

Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.

Subfields: Artificial Intelligence, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/55905e637d4c06cd16e331ac9417d93d896472fd

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. Yi Tay
2. Shuai Zhang
3. Aixin Sun
4. Lina Yao
5. Zhixu Li
6. Jieping Ye
7. Fuzhen Zhuang
8. Yanchi Liu

In Information Systems

1. Yi Tay
2. Aixin Sun
3. Lina Yao
4. Shuai Zhang
5. Jieping Ye
6. Fuzhen Zhuang
7. Yanchi Liu
8. Xiaofang Zhou

In Transportation

1. Jieping Ye
2. Yi Tay
3. Shuai Zhang
4. Aixin Sun
5. Lina Yao
6. Yanchi Liu
7. Xiaofang Zhou
8. Yu Zheng

Citations in the corpus

• Deep Learning Based Recommender System (2017, methodology): “ublication date: July 2018. 1:8 • S. Zhang et al. Fig. 1. Categories of deep neural network based recommendation models. Table 1. A lookup table for reviewed publications. Categories Publications MLP [2, 13, 20, 27, 38, 47, 53, 54, 66, 92, 95, 157, 166, 185], [12, 39, 93, 112, 134, 154, 182, 183] Au”
• Cross-domain Recommendation Without Sharing User-relevant Data (2019, unknown): “Some works [14, 36, 42, 44, 46] relied on neural networks to learn to match function between users and items directly, which can be regarded as the extension of traditional collaborative filtering approaches.”
• Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text (2019, unknown): “information [56].”
• Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation (2019, background): “Yang et al.(2017a) proposed a deep neural architecture named PACE for POI recommendation, which utilizes the smoothness of semi-supervised learning to alleviate the sparsity of collaborative filtering.”
• A review on deep learning for recommender systems: challenges and remedies (2018, unknown): “…Wang et al. (2015a) Zhang et al. (2016a) Hu et al. (2014) Huang et al. (2015) Paradarami et al. (2017) Zheng et al. (2016a) Kyo-Joong et al. (2014) Yang et al. (2017) Du et al. (2017) Tang et al. (2015) Lian et al. (2017) Barbieri et al. (2017) Suglia et al. (2017) Li and She (2017) Cao et al.…”
• Large-Scale Order Dispatch in On-Demand Ride-Hailing Platforms: A Learning and Planning Approach (2018, background): “Meanwhile, they also provide rich information on passenger demand and taxi mobility patterns, which can benefit various research areas including demand prediction, route planning, supply chain management, and traffic light control [2, 10, 20, 22].”