Session 14: Multi-task Recommendation

Date: Friday September 22, 11:15 AM – 12:35 PM (GMT+8)
Room: Hall 406D
Session Chair: Cataldo Musto
Parallel with: Session 13: Side Information, Items structure and Relations

• RESSTAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation
  by Wanda Li (Tsinghua University), Wenhao Zheng (Shopee Company), Xuanji Xiao (Shopee Company) and Suhang Wang (Penn State University).

  Recommendation systems play a vital role in many online platforms, with their primary objective being to satisfy and retain users. As directly optimizing user retention is challenging, multiple evaluation metrics are often employed. Existing methods generally formulate the optimization of these evaluation metrics as a multi-task learning problem, but often overlook the fact that user preferences for different tasks are personalized and change over time. Identifying and tracking the evolution of user preferences can lead to better user retention. To address this issue, we introduce the concept of “user lifecycle,” consisting of multiple stages characterized by users’ varying preferences for different tasks. We propose a novel \textbf{St}age-\textbf{A}daptive \textbf{N}etwork (\textbf{STAN}) framework for modeling user lifecycle stages. STAN first identifies latent user lifecycle stages based on learned user preferences, and then employs the stage representation to enhance multi-task learning performance. Our experimental results using both public and industrial datasets demonstrate that the proposed model significantly improves multi-task prediction performance compared to state-of-the-art methods, highlighting the importance of considering user lifecycle stages in recommendation systems. Furthermore, online A/B testing reveals that our model outperforms the existing model, achieving a significant improvement of 3.05\% in staytime per user and 0.88\% in CVR. These results indicate that our approach effectively improves the overall efficiency of the multi-task recommendation system.

  Full text in ACM Digital Library

• RESDisentangling Motives behind Item Consumption and Social Connection for Mutually-enhanced Joint Prediction
  by Youchen Sun (Nanyang Technological University), Zhu Sun (A*STAR), Xiao Sha (Nanyang Technological University), Jie Zhang (Nanyang Technological University) and Yew Soon Ong (Nanyang Technological University).

  Item consumption and social connection, as common user behaviors in many web applications, have been extensively studied. However, most current works separately perform either item or social link prediction tasks, possibly with the help of the other as an auxiliary signal. Moreover, they merely consider the behaviors in a holistic manner yet neglect the multi-faceted motives behind them (e.g., watching movies to kill time or with friends; connecting with others due to friendships or colleagues). To fill the gap, we propose to disentangle the multi-faceted motives in each network, defined respectively by the two types of behaviors, for mutually- enhanced joint prediction (DMJP). Specifically, we first learn the disentangled user representations driven by motives of multi-facets in both networks. Thereafter, the mutual influence of the two networks is subtly discriminated at the facet-to-facet level. The fine-grained mutual influence, proven to be asymmetric, is then exploited to help refine user representations in both networks, with the goal of achieving a mutually-enhanced joint item and social link prediction. Empirical studies on three public datasets showcase the superiority of DMJP against state-of-the-arts (SOTAs) on both tasks.

  Full text in ACM Digital Library

• RESBVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task Recommendation
  by Qianzhen Rao (Shenzhen University), Yang Liu (Shenzhen University), Weike Pan (Shenzhen University) and Zhong Ming (Shenzhen University).

  A practical recommender system should be able to handle heterogeneous behavioral feedback as inputs and has multi-task outputs ability. Although the heterogeneous one-class collaborative filtering (HOCCF) and multi-task learning (MTL) methods has been well studied, there is still a lack of targeted manner in their combined fields, i.e., Multi-behavior Multi-task Recommendation (MMR). To fill the gap, we propose a novel recommendation framework called Behavior-aware Variational AutoEncoder (BVAE), which meliorates the parameter sharing and loss minimization method with the VAE structure to address the MMR problem. Specifically, our BVAE includes address behavior-aware semi-encoders and decoders, and a target feature fusion network with a global feature filtering network, while using standard deviation to weigh loss. These modules generate the behavior-aware recommended item list via constructing better semantic feature vectors for users, i.e., from dual perspectives of behavioral preference and global interaction. In addition, we optimize our BVAE in terms of adaptability and robustness, i.e., it is concise and flexible to consume any amount of behaviors with different distributions. Extensive empirical studies on two real and widely used datasets confirm the validity of our design and show that our BVAE can outperform the state-of-the-art related baseline methods under multiple evaluation metrics.

  Full text in ACM Digital Library

• INDMCM: A Multi-task Pre-trained Customer Model for Personalization
  by Rui Luo (Amazon), Tianxin Wang (Amazon), Jingyuan Deng (Amazon) and Peng Wan (Amazon).

  Personalization plays a critical role in helping customers discover the products and contents they prefer for e-commerce stores.Personalized recommendations differ in contents, target customers, and UI. However, they require a common core capability – the ability to deeply understand customers’ preferences and shopping intents. In this paper, we introduce the MLCM (Multi-task Large pre-trained Customer Model), a large pre-trained BERT-based multi-task customer model with 10 million trainable parameters for e-commerce stores. This model aims to empower all personalization projects by providing commonly used preference scores for recommendations, customer embeddings for transfer learning, and a pre-trained model for fine-tuning. In this work, we improve the SOTA BERT4Rec framework to handle heterogeneous customer signals and multi-task training as well as innovate new data augmentation method that is suitable for recommendation task. Experimental results show that MLCM outperforms the original BERT4Rec by 17% on preference prediction tasks. Additionally, we demonstrate that the model can be easily fine-tuned to assist a specific recommendation task. For instance, after fine-tuning MLCM for an incentive based recommendation project, performance improves by 60% on the conversion prediction task and 25% on the click-through prediction task compared to the production baseline model.

  Full text in ACM Digital Library

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