Tag-Aware Personalized Recommendation Using a Hybrid Deep Model

By Zhenghua Xu, Thomas Lukasiewicz, Cheng Chen, Yishu Miao, Xiang-wu Meng. 2017

54 citations recorded by the source corpus.

Recently, many efforts have been put into tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping the tag-based user and item profiles to an abstract deep feature space, where the deep-semantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized). To ensure the scalability in practice, we further propose to improve this model’s training efficiency by using hybrid deep learning and negative sampling. Experimental results show that our approach can signifi-cantly outperform the state-of-the-art baselines in tag-aware personalized recommendation ( 3 . 8 times better than the best baseline), and that using hybrid deep learning and negative sampling can dramatically enhance the model’s training efficiency (hundreds of times quicker), while maintaining similar (and sometimes even better) training quality and recommendation performance.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 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. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Yi Tay
6. Kun Zhang
7. Tat-seng Chua
8. Aixin Sun

In Computer Vision and Pattern Recognition

1. Xiang Wang
2. Xiangnan He
3. Meng Wang
4. Yi Tay
5. Le Wu
6. Kun Zhang
7. Aixin Sun
8. Lina Yao

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Kun Zhang
6. Yi Tay
7. Tat-seng Chua
8. Aixin Sun

Citations in the corpus

• AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations (2020, methodology): “raction between user preferences and items features. Guo et al.[8] proposed an end-toend model named DeepFM to integrate factorization machine and Multilayer Perceptron (MLP) seamlessly. Xu et al.[32][33] fused tag annotations into personalized recommendation and proposed Deep Semantic Similarity ba”
• A review on deep learning for recommender systems: challenges and remedies (2018, background): “…et al. (2017) Yin et al. (2017) Shankar et al. (2017) Pana et al. (2017) He et al. (2017) Zhang et al. (2017c) Wang et al. (2017) Wang et al. (2016) Xu et al. (2017a) Volkovs et al. (2017) Rassweiler Filho et al. (2017) Vall et al. (2017) Nguyen et al. (2017) Hassan (2017) Seo et al.…”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, methodology): “By treating item content as raw features, such as bag-of-words and item tag representations, these models use autoencoders and their variants to learn the bottleneck hidden content representations of items [30], [54], [71], [72], [73], [74], [75], [76], [77], [78].”
• Deep Learning Based Recommender System (2017, methodology): “The authors [184] further improved DSPR using an autoencoder to learn low-dimensional representations from user/item profiles.”