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Micro Behaviors: A New Perspective in E-commerce Recommender Systems

Authors:

Dawei YinAuthors Info & Claims

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Pages 727 - 735

https://doi.org/10.1145/3159652.3159671

Published: 02 February 2018 Publication History

Abstract

The explosive popularity of e-commerce sites has reshaped users» shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to observe rich data about users. The majority of traditional recommender systems have focused on the macro interactions between users and items, i.e., the purchase history of a customer. However, within each macro interaction between a user and an item, the user actually performs a sequence of micro behaviors, which indicate how the user locates the item, what activities the user conducts on the item (e.g., reading the comments, carting, and ordering) and how long the user stays with the item. Such micro behaviors offer fine-grained and deep understandings about users and provide tremendous opportunities to advance recommender systems in e-commerce. However, exploiting micro behaviors for recommendations is rather limited, which motivates us to investigate e-commerce recommendations from a micro-behavior perspective in this paper. Particularly, we uncover the effects of micro behaviors on recommendations and propose an interpretable Recommendation framework RIB, which models inherently the sequence of mIcro Behaviors and their effects. Experimental results on datasets from a real e-commence site demonstrate the effectiveness of the proposed framework and the importance of micro behaviors for recommendations.

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Cited By

Liu QTang HWu LChao Z(2024)Deep Learning-Driven E-Commerce Marketing Communication for Recommending Shopping System and Optimizing User ExperienceJournal of Organizational and End User Computing10.4018/JOEUC.34325836:1(1-28)Online publication date: 22-May-2024
https://doi.org/10.4018/JOEUC.343258
Sanjay Suryawanshi N(2024)Predicting Consumer Behavior in E-Commerce Using Recommendation SystemsInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT19SEP1550(806-813)Online publication date: 21-Oct-2024
https://doi.org/10.38124/ijisrt/IJISRT19SEP1550
Pawan Kalyan KSoni PSrikanth TSingh MKumar Verma B(2024)A Comprehensive Review on Fashion Recommender System Using Machine Learning TechniquesSSRN Electronic Journal10.2139/ssrn.4485288Online publication date: 2024
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Index Terms

Micro Behaviors: A New Perspective in E-commerce Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

February 2018

821 pages

ISBN:9781450355810

DOI:10.1145/3159652

General Chairs:
Yi Chang
Jilin University, Huawei Inc.
,
Chengxiang Zhai
University of Illinois Urbana-Champaign
,
Program Chairs:
Yan Liu
University of Southern California
,
Yoelle Maarek
Amazon

Copyright © 2018 ACM.

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Published: 02 February 2018

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National Science Foundation (NSF)

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WSDM 2018

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WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining

February 5 - 9, 2018

CA, Marina Del Rey, USA

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WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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https://doi.org/10.4018/JOEUC.343258
Sanjay Suryawanshi N(2024)Predicting Consumer Behavior in E-Commerce Using Recommendation SystemsInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT19SEP1550(806-813)Online publication date: 21-Oct-2024
https://doi.org/10.38124/ijisrt/IJISRT19SEP1550
Pawan Kalyan KSoni PSrikanth TSingh MKumar Verma B(2024)A Comprehensive Review on Fashion Recommender System Using Machine Learning TechniquesSSRN Electronic Journal10.2139/ssrn.4485288Online publication date: 2024
https://doi.org/10.2139/ssrn.4485288
Li WLin XPan WMing Z(2024)Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688103(465-474)Online publication date: 8-Oct-2024
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Kao ZKailin XAnmin Z(2024)Behavior Interval Heterogeneous Sequential Recommendation2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI)10.1109/SEAI62072.2024.10674138(348-354)Online publication date: 21-Jun-2024
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