research-article

3D Convolutional Networks for Session-based Recommendation with Content Features

Authors:

Trinh Xuan Tuan,

Tu Minh PhuongAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 138 - 146

https://doi.org/10.1145/3109859.3109900

Published: 27 August 2017 Publication History

Abstract

In many real-life recommendation settings, user profiles and past activities are not available. The recommender system should make predictions based on session data, e.g. session clicks and descriptions of clicked items. Conventional recommendation approaches, which rely on past user-item interaction data, cannot deliver accurate results in these situations. In this paper, we describe a method that combines session clicks and content features such as item descriptions and item categories to generate recommendations. To model these data, which are usually of different types and nature, we use 3-dimensional convolutional neural networks with character-level encoding of all input data. While 3D architectures provide a natural way to capture spatio-temporal patterns, character-level networks allow modeling different data types using their raw textual representation, thus reducing feature engineering effort. We applied the proposed method to predict add-to-cart events in e-commerce websites, which is more difficult then predicting next clicks. On two real datasets, our method outperformed several baselines and a state-of-the-art method based on recurrent neural networks.

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Cited By

Selamat AIbrahim RAlrashidi MFujita H(2024)Social Recommender System Based on CNN Incorporating Tagging and Contextual FeaturesJournal of Cases on Information Technology10.4018/JCIT.33552426:1(1-20)Online publication date: 7-Jan-2024
https://dl.acm.org/doi/10.4018/JCIT.335524
He HYang XHuang FYi FLiang S(2024)GAT4Rec: Sequential Recommendation with a Gated Recurrent Unit and TransformersMathematics10.3390/math1214218912:14(2189)Online publication date: 12-Jul-2024
https://doi.org/10.3390/math12142189
Yang XEsquivel J(2024)Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business IntelligenceTsinghua Science and Technology10.26599/TST.2023.901002529:1(185-196)Online publication date: Feb-2024
https://doi.org/10.26599/TST.2023.9010025
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Index Terms

3D Convolutional Networks for Session-based Recommendation with Content Features
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 ACM.

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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Association for Computing Machinery

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Publication History

Published: 27 August 2017

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FPT Vietnam

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

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RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Selamat AIbrahim RAlrashidi MFujita H(2024)Social Recommender System Based on CNN Incorporating Tagging and Contextual FeaturesJournal of Cases on Information Technology10.4018/JCIT.33552426:1(1-20)Online publication date: 7-Jan-2024
https://dl.acm.org/doi/10.4018/JCIT.335524
He HYang XHuang FYi FLiang S(2024)GAT4Rec: Sequential Recommendation with a Gated Recurrent Unit and TransformersMathematics10.3390/math1214218912:14(2189)Online publication date: 12-Jul-2024
https://doi.org/10.3390/math12142189
Yang XEsquivel J(2024)Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business IntelligenceTsinghua Science and Technology10.26599/TST.2023.901002529:1(185-196)Online publication date: Feb-2024
https://doi.org/10.26599/TST.2023.9010025
Zhuo XQian SHu JDai FLin KWu G(2024)Multi-Hop Multi-View Memory Transformer for Session-Based RecommendationACM Transactions on Information Systems10.1145/366376042:6(1-28)Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1145/3663760
Wang JRathi PSundaram H(2024)A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity DynamicsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688145(433-443)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688145
Zhang QTeng ZWu DWang JSerra ESpezzano F(2024)An Enhanced Batch Query Architecture in Real-time RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680034(5078-5085)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680034
Zivic PVazquez HSánchez JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Scaling Sequential Recommendation Models with TransformersProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657816(1567-1577)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657816
Yu MZhu KZhao MYu JXu TJin DLi XYu R(2024)Learning Neighbor User Intention on User–Item Interaction Graphs for Better Sequential RecommendationACM Transactions on the Web10.1145/358052018:2(1-28)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3580520
Rehman AXu XZhu LWali MAmin MWei X(2024)Enhanced Session-Based Recommendation Using Multi-Channel Hypergraph Network2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)10.1109/YAC63405.2024.10598632(2035-2040)Online publication date: 7-Jun-2024
https://doi.org/10.1109/YAC63405.2024.10598632
Lin CChen SZeng MZhang SGao MLi H(2024)Shilling Black-Box Recommender Systems by Learning to Generate Fake User ProfilesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318321035:1(1305-1319)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3183210
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