Neural Multi-task Recommendation from Multi-behavior Data

Gao, Chen; He, Xiangnan; Gan, Dahua; Chen, Xiangning; Feng, Fuli; Li, Yong; Chua, Tat-Seng; Jin, Depeng

doi:10.1109/icde.2019.00140

Cited by 172 publications

(132 citation statements)

References 12 publications

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“…Instead of BPR, it incorporates this useful information into element-wise alternating least squares learner. More recently, a neural network approach is proposed by [6] to learn representations for user-item interactions with different behaviors. Multi-task learning is conducted to predict multi-behaviors with respect to a certain item in a cascading way.…”

Section: Related Workmentioning

confidence: 99%

Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Wang

Zhang

Liu

et al. 2020

Proceedings of the Web Conference 2020

110

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Session-based target behavior prediction aims to predict the next item to be interacted with specific behavior types (e.g., clicking). Although existing methods for session-based behavior prediction leverage powerful representation learning approaches to encode items' sequential relevance in a low-dimensional space, they suffer from several limitations. Firstly, they focus on only utilizing the same type of user behavior for prediction, but ignore the potential of taking other behavior data as auxiliary information. This is particularly crucial when the target behavior is sparse but important (e.g., buying or sharing an item). Secondly, item-to-item relations are modeled separately and locally in one behavior sequence, and they lack a principled way to globally encode these relations more effectively. To overcome these limitations, we propose a novel Multirelational Graph Neural Network model for Session-based target behavior Prediction, namely MGNN-SPred for short. Specifically, we build a Multi-Relational Item Graph (MRIG) based on all behavior sequences from all sessions, involving target and auxiliary behavior types. Based on MRIG, MGNN-SPred learns global itemto-item relations and further obtains user preferences w.r.t. current target and auxiliary behavior sequences, respectively. In the end, MGNN-SPred leverages a gating mechanism to adaptively fuse user representations for predicting next item interacted with target behavior. The extensive experiments on two real-world datasets demonstrate the superiority of MGNN-SPred by comparing with state-of-the-art session-based prediction methods, validating the benefits of leveraging auxiliary behavior and learning item-to-item relations over MRIG.

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Section: Related Workmentioning

confidence: 99%

Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Wang

Zhang

Liu

et al. 2020

Proceedings of the Web Conference 2020

110

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show abstract

“…Recommender systems have recently become prominent tools to provide personalized services for customers, so as to alleviate the information overload problem [3]. A number of recommenders [2,3,10,11] have been proposed to help infer users' potential interests based on their heterogeneous implicit feedback (HIF). Taking e-commerce as an example, the recommenders mainly leverage users' historical behaviors of various types (e.g., view, click, add-to-cart, purchase) to help predict what product to purchase afterwards.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Bob and Alice prefer add-to-cart before purchase, whilst Ella directly buys the 'Coat' after click without add-to-cart. (2) Repeated behaviors: users may perform certain behaviors several times over products, reflecting a reinforced preference to some degree. For example, Alice clicks twice to check details before she makes the purchase decision; Ella may be quite satisfied with the quality after purchasing the 'Coat' and directly buy another one for her friend.…”

Section: Introductionmentioning

confidence: 99%

“…They, however, are not well investigated by existing HIF based studies: (1) most methods [1,5,[11][12][13][14] directly ignore the temporal dynamics. They only model the influence of limited types of user behaviors (e.g., only view and purchase) via a weighted combination, which heavily restricts the generability of these methods; (2) although several approaches [2,10] notice the temporal dynamics, they simply pre-define a hard rule to order various user behaviors, thus failing to capture the real temporal dynamics. For instance, they assume that add-to-cart must come before purchase and after click.…”

Section: Introductionmentioning

confidence: 99%

“…To conclude, our main contributions lie in three folds: (1) to the best of our knowledge, we are the first to exploit temporal dynamics and repeated behaviors in HIF based recommendation; (2) we propose a novel end-to-end neural framework -TDRB, which delicately models the temporal dynamics and repeated behaviors for user preference prediction, thus achieving high-quality recommendation; and (3) extensive experiments on three real-world datasets show the superiority of our proposed TDRB, which significantly beats state-of-the-arts with a lift of 27.91%, 61.32% w.r.t. HR and NDCG on average, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling Temporal Dynamics and Repeated Behaviors for Recommendation

Zhou

Sun

Guo

et al. 2020

Advances in Knowledge Discovery and Data Mining

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Personalized recommendation has yield immense success in predicting user preference with heterogeneous implicit feedback (HIF), i.e., various user behaviors. However, existing studies consider less about the temporal dynamics and repeated patterns of HIF. They simply suppose:(1) a hard rule among user behaviors (e.g., add-to-cart must come before purchase and after view ); (2) merge repeated behaviors into one (e.g., view several times is considered as view once only), thus failing to unveil user preferences from their real behaviors. To ease these issues, we, therefore, propose a novel end-to-end neural framework -TDRB, which automatically models the Temporal Dynamics and Repeated Behaviors to assist in capturing user preference, thus achieving more accurate recommendations. Empirical studies on three real-world datasets demonstrate the superiority of our proposed TDRB against other state-of-the-arts.

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Gated Sequential Recommendation System with Social and Textual Information Under Dynamic Contexts

Geng

Gao

2021

Database Systems for Advanced Applications

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Neural Multi-task Recommendation from Multi-behavior Data

Cited by 172 publications

References 12 publications

Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Modelling Temporal Dynamics and Repeated Behaviors for Recommendation

Gated Sequential Recommendation System with Social and Textual Information Under Dynamic Contexts

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