Author name cluster

Duc-Trong Le

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6 papers

2 author rows

ECAI Conference 2025 Conference Paper

BRIDGE: Bundle Recommendation via Instruction-Driven Generation

Tuan-Nghia Bui
Huy-Son Nguyen
Cam-Van Thi Nguyen
Hoang-Quynh Le
Duc-Trong Le

Bundle recommendation aims to suggest a set of interconnected items to users. However, diverse interaction types and sparse interaction matrices often pose challenges for previous approaches in accurately predicting user-bundle adoptions. Inspired by the distant supervision strategy and generative paradigm, we propose BRIDGE, a novel framework for bundle recommendation. It consists of two main components, namely the item-sensitive instruction generation and the pseudo bundle generation modules. Inspired by the distant supervision approach, the former is to generate more auxiliary information, e. g. , sampled item-sensitive instruction, for training without using external data. This information is subsequently aggregated with collaborative signals from user historical interactions to create pseudo ‘ideal’ bundles. This capability allows BRIDGE to explore all aspects of bundles, rather than being limited to existing real-world bundles. It effectively bridging the gap between user imagination and predefined bundles, hence improving the bundle recommendation performance. Experimental results and analyses validate the superiority of BRIDGE over state-of-the-art methods across four benchmark datasets. Our implementation is available at https: //github. com/Rec4Fun/BRIDGE.

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ECAI Conference 2025 Conference Paper

RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction

Huy-Son Nguyen
Quang-Huy Nguyen
Duc-Hoang Pham
Duc-Trong Le
Hoang-Quynh Le
Padipat Sitkrongwong
Atsuhiro Takasu
Masoud Mansoury

Existing studies on bundle construction have relied merely on user feedback via bipartite graphs or enhanced item representations using semantic information. These approaches fail to capture elaborate relations hidden in real-world bundle structures, resulting in suboptimal bundle representations. To overcome this limitation, we propose RaMen, a novel method that provides a holistic multi-strategy approach for bundle construction. RaMen utilizes both intrinsic (characteristics) and extrinsic (collaborative signals) information to model bundle structures through Explicit Strategy-aware Learning (ESL) and Implicit Strategy-aware Learning (ISL). ESL employs task-specific attention mechanisms to encode multi-modal data and direct collaborative relations between items, thereby explicitly capturing essential bundle features. Moreover, ISL computes hyperedge dependencies and hypergraph message passing to uncover shared latent intents among groups of items. Integrating diverse strategies enables RaMen to learn more comprehensive and robust bundle representations. Meanwhile, Multi-strategy Alignment & Discrimination module is employed to facilitate knowledge transfer between learning strategies and ensure discrimination between items/bundles. Extensive experiments demonstrate the effectiveness of RaMen over state-of-the-art models on various domains, justifying valuable insights into complex item set problems.

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AAAI Conference 2021 Conference Paper

Modular Graph Transformer Networks for Multi-Label Image Classification

Hoang D. Nguyen
Xuan-Son Vu
Duc-Trong Le

With the recent advances in graph neural networks, there is a rising number of studies on graph-based multi-label classification with the consideration of object dependencies within visual data. Nevertheless, graph representations can become indistinguishable due to the complex nature of label relationships. We propose a multi-label image classification framework based on graph transformer networks to fully exploit inter-label interactions. The paper presents a modular learning scheme to enhance the classification performance by segregating the computational graph into multiple sub-graphs based on modularity. Our approach, named Modular Graph Transformer Networks (MGTN), is capable of employing multiple backbones for better information propagation over different sub-graphs guided by graph transformers and convolutions. We validate our framework on MS-COCO and Fashion550K datasets to demonstrate improvements for multilabel image classification. The source code is available at https: //github. com/ReML-AI/MGTN.

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IJCAI Conference 2019 Conference Paper

Correlation-Sensitive Next-Basket Recommendation

Duc-Trong Le
Hady W. Lauw
Yuan Fang

Items adopted by a user over time are indicative of the underlying preferences. We are concerned with learning such preferences from observed sequences of adoptions for recommendation. As multiple items are commonly adopted concurrently, e. g. , a basket of grocery items or a sitting of media consumption, we deal with a sequence of baskets as input, and seek to recommend the next basket. Intuitively, a basket tends to contain groups of related items that support particular needs. Instead of recommending items independently for the next basket, we hypothesize that incorporating information on pairwise correlations among items would help to arrive at more coherent basket recommendations. Towards this objective, we develop a hierarchical network architecture codenamed Beacon to model basket sequences. Each basket is encoded taking into account the relative importance of items and correlations among item pairs. This encoding is utilized to infer sequential associations along the basket sequence. Extensive experiments on three public real-life datasets showcase the effectiveness of our approach for the next-basket recommendation problem.

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IJCAI Conference 2018 Conference Paper

Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation

Duc-Trong Le
Hady W. Lauw
Yuan Fang

Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e. g. , clicks, bookmarks, purchases). Given a sequence of a particular type (e. g. , purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequence may be helped by also learning from another supporting sequence (e. g. , clicks). We develop three twin network structures modeling the generation of both target and support basket sequences. One based on "Siamese networks" facilitates full sharing of parameters between the two sequence types. The other two based on "fraternal networks" facilitate partial sharing of parameters. Experiments on real-world datasets show significant improvements upon baselines relying on one sequence type.

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IJCAI Conference 2017 Conference Paper

Basket-Sensitive Personalized Item Recommendation

Duc-Trong Le
Hady W. Lauw
Yuan Fang

Personalized item recommendation is useful in narrowing down the list of options provided to a user. In this paper, we address the problem scenario where the user is currently holding a basket of items, and the task is to recommend an item to be added to the basket. Here, we assume that items currently in a basket share some association based on an underlying latent need, e. g. , ingredients to prepare some dish, spare parts of some device. Thus, it is important that a recommended item is relevant not only to the user, but also to the existing items in the basket. Towards this goal, we propose two approaches. First, we explore a factorization-based model called BFM that incorporates various types of associations involving the user, the target item to be recommended, and the items currently in the basket. Second, based on our observation that various recommendations towards constructing the same basket should have similar likelihoods, we propose another model called CBFM that further incorporates basket-level constraints. Experiments on three real-life datasets from different domains empirically validate these models against baselines based on matrix factorization and association rules.

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