Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation

Zhu, Nengjun; Cao, Jian; Liu, Yanchi; Yang, Yang; Ying, Haochao; Xiong, Hui

doi:10.1145/3336191.3371840

Cited by 35 publications

(4 citation statements)

References 28 publications

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“…Besides, researchers exploited hierarchical attention networks to learn better short term user preference with featurelevel attention and item level attention [223]. For the long time user interest modeling, researchers proposed to leverage nearby sessions [227], designed attention modeling or memory addressing techniques to find related sessions [229], [236], [237], [249].…”

Section: Temporal and Session Based Recommendationmentioning

confidence: 99%

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Wu,

He,

Wang

et al. 2021

Preprint

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Influenced by the stunning success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models, aiming to summarize the field to facilitate future progress. Distinct from existing surveys that categorize existing methods based on the taxonomy of deep learning techniques, we instead summarize the field from the perspective of recommendation modeling, which could be more instructive to researchers and practitioners working on recommender systems. Specifically, we divide the work into three types based on the data they used for recommendation modeling: 1) collaborative filtering models, which leverage the key source of user-item interaction data; 2) content enriched models, which additionally utilize the side information associated with users and items, like user profile and item knowledge graph; and 3) context enriched models, which account for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative works for each type, we finally discuss some promising directions in this field, including benchmarking recommender systems, graph reasoning based recommendation models, and explainable and fair recommendations for social good.

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Section: Temporal and Session Based Recommendationmentioning

confidence: 99%

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Wu,

He,

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The model needs extra information about user's behaviors and thus does not fit for our problem. The most related work to ours is [35]. In this work, the authors model the session recommendation issue as a a hierarchical decision-making process, i.e., user first chooses an intention, and then clicks items conditionally relied on the previous clicked item.…”

Section: Related Workmentioning

confidence: 99%

DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation

Yang¹,

Luo

Xin³

et al. 2021

Preprint

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Session-based recommendations have been widely adopted for various online video and E-commerce Websites. Most existing approaches are intuitively proposed to discover underlying interests or preferences out of the anonymous session data. This apparently ignores the fact these sequential behaviors usually reflect session user's potential demand, i.e., a semantic level factor, and therefore how to estimate underlying demands from a session is challenging. To address aforementioned issue, this paper proposes a demand-aware graph neural networks (DAGNN). Particularly, a demand modeling component is designed to first extract session demand and the underlying multiple demands of each session is estimated using the global demand matrix. Then, the demand-aware graph neural network is designed to extract session demand graph to learn the demandaware item embedddings for the later recommendations. The mutual information loss is further designed to enhance the quality of the learnt embeddings. Extensive experiments are evaluated on several real-world datasets and the proposed model achieves the SOTA model performance.

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“…Specifically, in the convolutional layer, we use one-dimensional convolution kernels of sizes 2, 3, 4, and 5. Each size uses 32 filters, so after pooling, 128-dimensional features will be obtained [15]. Since the link sequence is relatively long, we set up a two-layer convolution structure with convolution steps of 1 and 2, and finally, each layer has 128-dimensional features.…”

Section: Modeling Schemementioning

confidence: 99%

Complementary Fusion of Deep Network and Tree Model for ETA Prediction

Huang

Zhang²,

Bao

et al. 2021

Proceedings of the 29th International Conference on Advances in Geographic Information Systems

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Estimated time of arrival (ETA) is a very important factor in the transportation system. It has attracted increasing attentions and has been widely used as a basic service in navigation systems and intelligent transportation systems. In this paper, we propose a novel solution to the ETA estimation problem, which is an ensemble on tree models and neural networks. We proved the accuracy and robustness of the solution on the A/B list and finally won first place in the SIGSPATIAL 2021 GISCUP competition.

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Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation

Cited by 35 publications

References 28 publications

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation

Complementary Fusion of Deep Network and Tree Model for ETA Prediction

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