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Package Recommendation with Intra- and Inter-Package Attention Networks

Published: 11 July 2021 Publication History

Abstract

With the booming of online social networks in the mobile internet, an emerging recommendation scenario has played a vital role in information acquisition for user, where users are no longer recommended with a single item or item list, but a combination of heterogeneous and diverse objects (called a package, e.g., a package including news, publisher, and friends viewing the news). Different from the conventional recommendation where users are recommended with the item itself, in package recommendation, users would show great interests on the explicitly displayed objects that could have a significant influence on the user behaviors. However, to the best of our knowledge, few effort has been made for package recommendation and existing approaches can hardly model the complex interactions of diverse objects in a package. Thus, in this paper, we make a first study on package recommendation and propose an Intra- and inter-package attention network for Package Recommendation (IPRec). Specifically, for package modeling, an intra-package attention network is put forward to capture the object-level intention of user interacting with the package, while an inter-package attention network acts as a package-level information encoder that captures collaborative features of neighboring packages. In addition, to capture users preference representation, we present a user preference learner equipped with a fine-grained feature aggregation network and coarse-grained package aggregation network. Extensive experiments on three real-world datasets demonstrate that IPRec significantly outperforms the state of the arts. Moreover, the model analysis demonstrates the interpretability of our IPRec and the characteristics of user behaviors. Codes and datasets can be obtained at https://github.com/LeeChenChen/IPRec.

Supplementary Material

MP4 File (SIGIR21-fp0161.mp4)
Presentation video of SIGIR2021 paper "Package Recommendation with Intra- and Inter-Package Attention Networks".

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cover image ACM Conferences
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2021
2998 pages
ISBN:9781450380379
DOI:10.1145/3404835
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 July 2021

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Author Tags

  1. neural networks
  2. recommendation systems
  3. social influence

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  • Research-article

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  • the National Natural Science Foundation of China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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  • (2024)Boosting Healthiness Exposure in Category-Constrained Meal Recommendation Using Nutritional StandardsACM Transactions on Intelligent Systems and Technology10.1145/364385915:4(1-28)Online publication date: 5-Feb-2024
  • (2024)Towards Hierarchical Intent Disentanglement for Bundle RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332917536:7(3556-3567)Online publication date: Jul-2024
  • (2024)An Approach for Privacy-Aware Mobile App Package RecommendationIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.34430285:12(6240-6252)Online publication date: Dec-2024
  • (2024)Graph Representation Learning for Recommendation Systems: A Short ReviewAdvances in Information Systems, Artificial Intelligence and Knowledge Management10.1007/978-3-031-51664-1_3(33-48)Online publication date: 20-Jan-2024
  • (2023)User-Meal Interaction Learning for Meal Recommendation: A Reproducibility StudyProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625342(104-113)Online publication date: 26-Nov-2023
  • (2023)Recommendation System: A Survey and New PerspectivesWorld Scientific Annual Review of Artificial Intelligence10.1142/S281103232330001301Online publication date: 4-May-2023
  • (2023)Category-Wise Meal RecommendationNeural Information Processing10.1007/978-981-99-8181-6_22(282-294)Online publication date: 27-Nov-2023
  • (2023)BR: Category-Aware Cross-View Contrastive Learning Framework for Bundle RecommendationDatabase Systems for Advanced Applications. DASFAA 2023 International Workshops10.1007/978-3-031-35415-1_14(194-203)Online publication date: 17-Apr-2023
  • (2022)CrossCBRProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539229(1233-1241)Online publication date: 14-Aug-2022
  • (2022)Group-based social diffusion in recommendationWorld Wide Web10.1007/s11280-022-01079-226:4(1775-1792)Online publication date: 25-Oct-2022
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