Controllable Multi-Interest Framework for Recommendation

Cen, Yukuo; Zhang, Jianwei; Zou, Xu; Zhou, Chang; Yang, Hongxia; Tang, Jie

doi:10.1145/3394486.3403344

Cited by 215 publications

(196 citation statements)

References 43 publications

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“…Beyond accuracy: In health RS, accuracy is sometimes not the only important indicator [28], [52]. For example, during the dietary recommendation, novelty, richness, and cost are also several important indicators for users to consider.…”

Section: Challengesmentioning

confidence: 99%

An Overview of Recommendation Techniques and Their Applications in Healthcare

Yue

Wang

Zhang

et al. 2021

IEEE/CAA J. Autom. Sinica

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With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, CF-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.

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Section: Challengesmentioning

confidence: 99%

An Overview of Recommendation Techniques and Their Applications in Healthcare

Yue

Wang

Zhang

et al. 2021

IEEE/CAA J. Autom. Sinica

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“…STAMP (Liu et al , 2018) captures the user’s long-term overall and current short-term interest preferences from behavior items. There are also efforts (Chen et al , 2019a; Li et al , 2019; Cen et al , 2020) to capture multiple interest (or intent) representation in user behavior items. Although our work is deeper than modeling intent, which aims to explore the user’s mind, there is still a lot to learn from and refer to.…”

Section: Related Workmentioning

confidence: 99%

Behavioral data assists decisions: exploring the mental representation of digital-self

Zhang

Cui

et al. 2021

IJCS

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Purpose The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell. Design/methodology/approach In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making. Findings The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest. Originality/value In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

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“…DSIN [4] highlights that user behaviors are highly homogeneous in each session and heterogeneous cross sessions and designs a self-attention network with bias encoding to get the corresponding interest representation of each session. MIND [6] and ComiRec [1] emphasize that a single interest vector is insufficient to capture the varying nature of users' interests. They exploit capsule network and dynamic routing to obtain the representation of users' interests as multiple interest vectors.…”

Section: Related Workmentioning

confidence: 99%

“…DIN and its successors achieve better performance than Embedding&MLP methods, but a single interest vector is still insufficient to capture the varying characteristics of users' interests. To address this problem, MIND [6] and ComiRec [1] leverage capsule routing mechanism for clustering historical behaviors and obtaining users' multiple interest vectors. However, except item-ids, category-ids and similar id features, both of them lack the modeling of finer grained features like color and materials.…”

Section: Introductionmentioning

confidence: 99%

Multiple Interest and Fine Granularity Network for User Modeling

Xie¹,

Wei²,

Hua³

et al. 2021

Preprint

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User modeling plays a fundamental role in industrial recommender systems, either in the matching stage and the ranking stage, in terms of both the customer experience and business revenue. How to extract users' multiple interests effectively from their historical behavior sequences to improve the relevance and personalization of the recommend results remains an open problem for user modeling. Most existing deep-learning based approaches exploit item-ids and category-ids but neglect fine-grained features like color and material, which hinders modeling the fine granularity of users' interests.In the paper, we present Multiple interest and Fine granularity Network (MFN), which tackle users' multiple and fine-grained interests and construct the model from both the similarity relationship and the combination relationship among the users' multiple interests. Specifically, for modeling the similarity relationship, we leverage two sets of embeddings, where one is the fixed embedding from pretrained models (e.g. Glove) to give the attention weights and the other is trainable embedding to be trained with MFN together. For modeling the combination relationship, self-attentive layers are exploited to build the higher order combinations of different interest representations. In the construction of network, we design an interest-extract module using attention mechanism to capture multiple interest representations from user historical behavior sequences and leverage an auxiliary loss to boost the distinction of the interest representations. Then a hierarchical network is applied to model the attention relation between the multiple interest vectors of different granularities and the target item. We evaluate MFN on both public and industrial datasets. The experimental results demonstrate that the proposed MFN achieves superior performance than other existed representing methods.

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Controllable Multi-Interest Framework for Recommendation

Cited by 215 publications

References 43 publications

An Overview of Recommendation Techniques and Their Applications in Healthcare

An Overview of Recommendation Techniques and Their Applications in Healthcare

Behavioral data assists decisions: exploring the mental representation of digital-self

Multiple Interest and Fine Granularity Network for User Modeling

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