AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System

Xiao Kecheng
Xiao Kecheng
Zhang Yuanxing
Zhang Yuanxing
Yan Wei
Yan Wei
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We introduce AMER for automatic behavior modeling and interaction exploration in recommender systems to relieve the human efforts from feature engineering and architecture engineering

Abstract:

User behavior and feature interactions are crucial in deep learning-based recommender systems. There has been a diverse set of behavior modeling and interaction exploration methods in the literature. Nevertheless, the design of task-aware recommender systems still requires feature engineering and architecture engineering from domain exp...More

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Introduction
  • Recommender system is responsible for presenting items that match users’ interests from a large number of candidates, which has become an essential part of today’s online E-commerce websites and content platform.
  • Facing the increasing number of users, items, and user-item interactions, more and more real-world applications [11, 84] resort to powerful deep learning models to discover personalized content.
  • Among the methods for encoding user’s behaviors, recurrent neural network (RNN) [26, 27] is hard to preserve long-term behavioral dependencies even though employing gated memory cells [9, 28].
  • The attention mechanism [2] is able to directly aggregate features from entire
Highlights
  • Recommender system is responsible for presenting items that match users’ interests from a large number of candidates, which has become an essential part of today’s online E-commerce websites and content platform
  • We introduce AMER for Automatic behavior Modeling on the user’s sequential history as well as low-order and high-order interaction Exploration on the categorical features in the Recommender system
  • The experimental results over various recommendation scenarios show that our automatic paradigms consistently achieves state-of-the-art performance despite the influence of multiple runs and could even outperform the strongest baselines with elaborate manual design, indicating both effectiveness and robustness of AMER’s search space and searching pipeline
  • We introduce AMER for automatic behavior modeling and interaction exploration in recommender systems to relieve the human efforts from feature engineering and architecture engineering
  • Owing to the three-stage search space and the matched three-step one-shot searching pipeline, AMER covers most of representative recommendation models and outperforms the competitive methods in various scenarios, demonstrating both effectiveness and robustness of the proposed method
  • We introduce AMER for automatic behavior modeling and interaction exploration in the deep learning based recommender systems
Methods
  • The authors compare AMER with various representative baseline methods.
  • The layer number Lb of AMER is set to 6 to cover representative recommendation methods and one-shot model is trained with learning rate of 1e-3 for 4000 epochs on Beauty, 2000 epochs on Steam under 1:1 negative sampling rate.
  • In steps 2 and 3, AMER conducts the same interaction exploration and MLP investigation as non-sequential datasets despite the base MLP is set to [200, 80] and only top-5 interactions are reserved in the final model.
Results
  • Evaluation Metrics

    To quantify the performance of sequential models, the authors use HR (Hit Ratio) and NDCG (Normalized Discounted Cumulative Gain) in sequential datasets.
  • For non-sequential and hybrid datasets, the authors use standard metrics of AUC (Area Under ROC Curve) and log loss
Conclusion
  • The authors introduce AMER for automatic behavior modeling and interaction exploration in recommender systems to relieve the human efforts from feature engineering and architecture engineering.
  • Owing to the three-stage search space and the matched three-step one-shot searching pipeline, AMER covers most of representative recommendation models and outperforms the competitive methods in various scenarios, demonstrating both effectiveness and robustness of the proposed method
Summary
  • Introduction:

    Recommender system is responsible for presenting items that match users’ interests from a large number of candidates, which has become an essential part of today’s online E-commerce websites and content platform.
  • Facing the increasing number of users, items, and user-item interactions, more and more real-world applications [11, 84] resort to powerful deep learning models to discover personalized content.
  • Among the methods for encoding user’s behaviors, recurrent neural network (RNN) [26, 27] is hard to preserve long-term behavioral dependencies even though employing gated memory cells [9, 28].
  • The attention mechanism [2] is able to directly aggregate features from entire
  • Methods:

    The authors compare AMER with various representative baseline methods.
  • The layer number Lb of AMER is set to 6 to cover representative recommendation methods and one-shot model is trained with learning rate of 1e-3 for 4000 epochs on Beauty, 2000 epochs on Steam under 1:1 negative sampling rate.
  • In steps 2 and 3, AMER conducts the same interaction exploration and MLP investigation as non-sequential datasets despite the base MLP is set to [200, 80] and only top-5 interactions are reserved in the final model.
  • Results:

    Evaluation Metrics

    To quantify the performance of sequential models, the authors use HR (Hit Ratio) and NDCG (Normalized Discounted Cumulative Gain) in sequential datasets.
  • For non-sequential and hybrid datasets, the authors use standard metrics of AUC (Area Under ROC Curve) and log loss
  • Conclusion:

    The authors introduce AMER for automatic behavior modeling and interaction exploration in recommender systems to relieve the human efforts from feature engineering and architecture engineering.
  • Owing to the three-stage search space and the matched three-step one-shot searching pipeline, AMER covers most of representative recommendation models and outperforms the competitive methods in various scenarios, demonstrating both effectiveness and robustness of the proposed method
Tables
  • Table1: Comparison with representative methods on the sequential datasets
  • Table2: Comparison with representative methods on the non-sequential datasets
  • Table3: Ablation study on the aggregation MLP
  • Table4: Comparison with representative methods on the hybrid dataset
  • Table5: Statistics of the sequential datasets
  • Table6: Statistics of the non-sequential datasets
  • Table7: Statistics of the Alimama dataset
  • Table8: Searched blocks and corresponding experimental results of behavior modeling (AMER-1) on Beauty and Steam over 4 runs. Each block i denotes the residual block of i-th layer, which is represented by either a tuple of (layer operation, activation operation, normalization operation) or a “Zero” operation (skip this layer)
  • Table9: Searched interactions, MLPs and corresponding experimental results of interaction exploration (AMER2) and additional aggregation MLP investigation (AMER-23) on Criteo and Avazu over 4 runs. Each interaction is represented by a tuple of non-sequential feature fields. Each MLP includes a list of hidden layers, where each hidden layer is represented by a tuple of (hidden size, activation operation)
  • Table10: Searched blocks, interactions, MLPs and corresponding experimental results of behavior modeling (AMER-1), additional interaction exploration (AMER-12) and further aggregation MLP investigation (AMER123) on Alimama over 4 runs. Each block i denotes the residual block of i-th layer, which is represented by either a tuple of (layer operation, activation operation, normalization operation) or a “Zero” operation (skip this layer). Each interaction is represented by a tuple of non-sequential feature fields. Each MLP includes a list of hidden layers, where each hidden layer is represented by a tuple of (hidden size, activation operation)
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Funding
  • The experimental results over various recommendation scenarios show that our automatic paradigms consistently achieves state-of-the-art performance despite the influence of multiple runs and could even outperform the strongest baselines with elaborate manual design, indicating both effectiveness and robustness of AMER’s search space and searching pipeline
Study subjects and analysis
times on all datasets: 4
The one-shot models and derived model are trained with 1e-5 learning rate, and one-shot weights are used to initialize the weights of derived model. By conforming to [37, 44, 61], we run AMER for 4 times on all datasets and report the metrics across 4 runs as the final results. More implementation details can be found in Appendix D.In Appendix A, we conduct a detailed analysis of the representative recommender systems and show that these methods can be obtained in AMER’s search space

times on all datasets: 4
The one-shot models and derived model are trained with 1e-5 learning rate, and one-shot weights are used to initialize the weights of derived model. By conforming to [37, 44, 61], we run AMER for 4 times on all datasets and report the metrics across 4 runs as the final results. More implementation details can be found in Appendix D

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