LARA: Attribute-to-feature Adversarial Learning for New-item Recommendation

By Changfeng Sun, Han Liu, Zhaochun Ren, Tian Gan, Liqiang Nie. 2020

61 citations recorded by the source corpus.

Recommending new items in real-world e-commerce portals is a challenging problem as the cold start phenomenon, i.e., lacks of user-item interactions. To address this problem, we propose a novel recommendation model, i.e., adversarial neural network with multiple generators, to generate users from multiple perspectives of items' attributes. Namely, the generated users are represented by attribute-level features. As both users and items are attribute-level representations, we can implicitly obtain user-item attribute-level interaction information. In light of this, the new item can be recommended to users based on attribute-level similarity. Extensive experimental results on two item cold-start scenarios, movie and goods recommendation, verify the effectiveness of our proposed model as compared to state-of-the-art baselines.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Recommender 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. Tat-seng Chua
4. Hongye Jin
5. An Zhang
6. Tong Xu
7. Liqiang Nie
8. Dacheng Tao

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Liqiang Nie
5. Hongye Jin
6. An Zhang
7. Tong Xu
8. Fuli Feng

In Management Science and Operations Research

1. Xiang Wang
2. Xiangnan He
3. Tat-seng Chua
4. Dacheng Tao
5. An Zhang
6. Xianglong Liu
7. Meng Wang
8. Fuli Feng

In Recommender Systems

1. Xiang Wang
2. Xiangnan He
3. Tat-seng Chua
4. Dacheng Tao
5. Yongfeng Zhang
6. Liqiang Nie
7. Fuli Feng
8. Xu Chen

Citations in the corpus

• Regionwise Generative Adversarial Image Inpainting for Large Missing Areas (2019, methodology): “The LARA [42] utilized an adversarial neural network with multiple generators to generate users from multiple perspectives of items’ attributes.”
• Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System (2023, background): “The LLMs-augmented recommender system introduced above can be used to address several challenging tasks, that are hard or even impossible to be addressed with conventional recommender systems, such as cross-domain recommendation [26] and cold-start recommendation [17].”
• Disentangled Graph Collaborative Filtering (2020, background): “history with users [21], item knowledge [15, 32, 39, 41, 42], and user reviews [5, 22], or conduct psychology experiments [24] to establish the ground truth on user intents and further interpret disentangled representations better.”