Dual Part-pooling Attentive Networks for Session-based Recommendation

Zhang, Xiaokun; Lin, Hongfei; Yang, Liang; Xu, Bo; Diao, Yuanan; Ren, Lu

doi:10.1016/j.neucom.2021.01.092

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…Wang et al [26] proposed a global context-enhanced graph neural network model (GCE-GNN), which uses global graph and session graph to learn the global context information and local context information of the current session, respectively. Zhang et al [27] proposed a dual part-pooling attentive networks for session-based recommendation (DPAN4Rec), which applies sequential acquisition and collective acquisition to capture sequential dependencies and collective dependencies in sessions, respectively. Choi et al [28] proposed session-aware linear item similarity/transition model (SLIST) for considering the holistic aspects of the sessions.…”

Section: Collaborative Information-based Methodsmentioning

confidence: 99%

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Session-Based Recommendation with GNN and Time-Aware Memory Network

Yan

Kang

Zeng

et al. 2022

Mobile Information Systems

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The goal of the session-based recommendation system (SBRS) is to predict the user’s next behavior based on anonymous sessions. Since long-term historical information of users is not available, deep learning technology has become the mainstream technology in session-based recommendation systems instead of traditional content-based recommendation methods. However, most SBRS methods only consider the session itself, ignoring the collaborative information from other sessions. Even if some SBRS models consider collaborations between sessions, they mostly use the click order to calculate the similarity only and ignore the time the user spends on different items, which might imply the user’s varying interest on these items. In this paper, we propose a session-based recommendation model with GNN and time-aware memory networks (SR-GTM), which learns the user’s interest representation by combining the information from the session itself and the collaborative information from relevant neighbor sessions. Specifically, SR-GTM mainly includes inner feature extraction module (IFEM) and outer feature extraction module (OFEM). IFEM uses GNN to learn the session features based on its item sequence, and OFEM uses a memory network with dwell time information encoded to extract collaborative information. Finally, SR-GTM aggregates IFEM and OFEM by the gating mechanism and then decodes the output by a softmax layer to obtain the recommendation score for each candidate item. Experiments on three public datasets Yoochoose1/64, Yoochoose1/4, and RetailRocket show that SR-GTM achieves optimal performance compared with other state-of-the-art methods. More specifically, SR-GTM has improvements of 0.77%, 0.38%, and 3.63% over the best baseline method in P@20 and has improvements of 2.91%, 2.52%, and 2.49% in MRR@20, respectively.

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Section: Collaborative Information-based Methodsmentioning

confidence: 99%

“…In order to verify the effectiveness of SR-GTM, we compare it with the following baselines: item-KNN [4], FPMC [21], GRU4REC [3], NARM [10], STAMP [11], SR-GNN [12], DPAN4Rec [27], SLIST [28], NISER+ [30], CSRM [15], and STAN [16].…”

Section: Baseline Methodsmentioning

confidence: 99%

Session-Based Recommendation with GNN and Time-Aware Memory Network

Yan

Kang

Zeng

et al. 2022

Mobile Information Systems

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“…With the rapid development of recurrent neural networks, Hidasi et al (2015) proposed GRU4REC, which uses GRU to process and model the time features of a session sequence. Zhang et al (2021) stack GRU as an encoder to extract information and then aggregate it as a session embedding through partial pooling of attention layers. However, sequential/session-based recommendations based on the recurrent neural network can only capture the user's time interest features.…”

Section: Session-based Recommendationmentioning

confidence: 99%

Multi-relation global context learning for session-based recommendation

Cao

2023

DTA

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PurposeSession-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users.Design/methodology/approachThis work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight.FindingsWe did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model.Originality/valueFirst, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.

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“…Attention mechanism is incorporated into the neural network to distinguish the importance of different items within a session. Co-CoRec [21] leverages category information to capture the context-aware action dependence and uses a selfattention network to capture item-to-item transition patterns within each category-specific subsequence. DPAN [22] applies an attention network to model both the collective and sequential information within sessions.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Next-Item Recommendation Through Adaptive User Group Modeling

Zhu,

Sun,

Cao

et al. 2023

J. Soc. Comput.

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Session-based recommender systems are increasingly applied to next-item recommendations.However, existing approaches encode the session information of each user independently and do not consider the interrelationship between users. This work is based on the intuition that dynamic groups of likeminded users exist over time. By considering the impact of latent user groups, we can learn a user's preference in a better way. To this end, we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user groups. Specifically, we utilize two network units to learn users' long and short-term sessions, respectively. Meanwhile, we employ two additional units to determine the affiliation of users with specific latent groups, followed by an aggregation of these latent group representations. Finally, user preference representations are shaped comprehensively by considering all these four aspects, based on an attention mechanism. Moreover, to avoid setting the number of groups manually, we further incorporate an adaptive learning unit to assess the necessity for creating a new group and learn the representation of emerging groups automatically. Extensive experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall, mean average precision (mAP), and area under curve (AUC) metrics.

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Dual Part-pooling Attentive Networks for Session-based Recommendation

Cited by 12 publications

References 8 publications

Session-Based Recommendation with GNN and Time-Aware Memory Network

Session-Based Recommendation with GNN and Time-Aware Memory Network

Multi-relation global context learning for session-based recommendation

Enhancing Next-Item Recommendation Through Adaptive User Group Modeling

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