Abstract
Recommender system (RecSys) [3], which attempts to find what items a particular user wants, is an important area in data mining and machine learning. However, as the recommender task is getting more diverse and the recommending models are growing more complicated, it is more and more difficult to develop a proper RecSys that can adapt well to a new recommender task. Recently, the automated machine learning (AutoML) [1, 2], which targets at easing the usage of learning tools and designing task-dependent learning models, has become an important and popular area with both practical needs and research values.
Modern RecSys contains two phases, matching, and ranking (Figure 1). In the first phase, collaborative filtering (CF) models are employed to extract thousands of items from billion-scale items. The data input of this phase is mainly the user-item interaction history. Thus, the key to CF models is to compute the similarity between a user and an item through a designed interaction function. Considering the various task settings, i.e. datasets and evaluation metrics, the best choice of interaction function can vary, which can be searched by AutoML. In the second phase, feature-based recommender models, also known as click-through rate (CTR) prediction models, are utilized to further rank the output of the matching model. One of the most significant operations in this phase is to generate effective features for users and items given the input data. Therefore, AutoML-based feature generation methods can be deployed. From a wider perspective, the model training in both two phases is faced with the high cost of hyper-parameter tuning. Hyper-parameter optimization, a major direction of AutoML can help reduce human efforts in tuning recommendation models. On the other hand, since the the user-item interactions can be naturally modeled as a bipartite graph, graph neural networks (GNNs) have been widely used in RecSys. Then, AutoML, especially neural architecture search (NAS), can help reduce such efforts. Above all, AutoML can help to build RecSys from these four aspects (see red texts in Figure 1).
Figure 1: How AutoML and RecSys integrate and mutually benefit with each other.
Schedule
Time | Event |
---|---|
8:00-8:40 | Part 1: An introduction to Automated Machine Learning (AutoML). [Slides] |
Speaker: Quanming Yao | |
8:40-9:20 | Part 2: Why AutoML is Needed in RecSys and Recent Advances [Slides] |
Speaker: Chen Gao | |
9:20-9:30 | Break |
9:30-10:10 | Part 3: Automated Graph Neural Network for RecSys. [Slides] |
Speaker: Huan Zhao | |
10:10-10:50 | Part 4: Automated Knowledge Graph Embedding. [Slides] |
Speaker: Yongqi Zhang | |
10:50-11:00 | Part 5: Discussion |
Organizers
Quanming Yao, EE Department, Tsinghua University / 4Paradigm Inc. Beijing. China.
Yong Li, Department of Electronic Engineering, Tsinghua University, Beijing. China.
Chen Gao, Department of Electronic Engineering, Tsinghua University, Beijing. China.
Huan Zhao, 4Paradigm Inc. Beijing. China.
Yongqi Zhang, 4Paradigm Inc. Beijing. China.
Past Tutorial
- KDD 2020 Tutorial: Advances in Recommender Systems
References
Due to the space limitation, we only list highly-related papers.
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