Dual-Embedding based Deep Latent Factor Models for Recommendation
| Autor: | Yanyan Shen, Weiyu Cheng, Linpeng Huang, Yanmin Zhu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science business.industry media_common.quotation_subject Perspective (graphical) 02 engineering and technology DUAL (cognitive architecture) Machine learning computer.software_genre 020204 information systems Negative feedback 0202 electrical engineering electronic engineering information engineering Collaborative filtering Embedding 020201 artificial intelligence & image processing Quality (business) Artificial intelligence business Preference (economics) computer Factor analysis media_common |
| Zdroj: | ACM Transactions on Knowledge Discovery from Data. 15:1-24 |
| ISSN: | 1556-472X 1556-4681 |
| DOI: | 10.1145/3447395 |
| Popis: | Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to the recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this article proposes a dual-embedding based deep latent factor method for recommendation with implicit feedback. In addition to learning a primitive embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users) and propose attentive neural methods to discriminate the importance of interacted users/items for dual-embedding learning. We design two dual-embedding based deep latent factor models, DELF and DESEQ, for pure collaborative filtering and temporal collaborative filtering (i.e., sequential recommendation), respectively. The novel attempt of the proposed models is to capture each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on four real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of our methods on item recommendation. |
| Databáze: | OpenAIRE |
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