Vijaikumar, M and Shevade, S and Murty, MN (2021) GRAM-SMOT: Top-N Personalized Bundle Recommendation via Graph Attention Mechanism and Submodular Optimization. In: European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, 14-18 Sep 2020, pp. 297-313.
Full text not available from this repository.Abstract
Bundle recommendation � recommending a group of products in place of individual products to customers is gaining attention day by day. It presents two interesting challenges � (1) how to personalize and recommend existing bundles to users, and (2) how to generate personalized novel bundles targeting specific users. Recently, few models have been proposed for modeling the bundle recommendation problem. However, they have the following shortcomings. First, they do not consider the higher-order relationships amongst the entities (users, items and bundles). Second, they do not model the relative influence of items present in the bundles, which is crucial in defining such bundles. In this work, we propose GRAM-SMOT � a graph attention-based framework to address the above challenges. Further, we define a loss function based on the metric-learning approach to learn the embeddings of entities efficiently. To generate novel bundles, we propose a strategy that leverages submodular function maximization. To analyze the performance of the proposed model, we conduct comprehensive experiments on two real-world datasets. The experimental results demonstrate the superior performance of the proposed model over the existing state-of-the-art models. © 2021, Springer Nature Switzerland AG.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH |
| Keywords: | Data mining, Attention mechanisms; Higher-order; Loss functions; Metric learning; Real-world datasets; State of the art; Submodular functions; Submodular optimizations, Machine learning |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 07 Jul 2021 07:29 |
| Last Modified: | 07 Jul 2021 07:29 |
| URI: | http://eprints.iisc.ac.in/id/eprint/68740 |
Actions (login required)
 |
View Item |
