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Does product recommendation meet its waterloo in unexplored categories?: no, price comes to help

Published: 03 July 2014 Publication History

Abstract

State-of-the-art methods for product recommendation encounter significant performance drop in categories where a user has no purchase history. This problem needs to be addressed since current online retailers are moving beyond single category and attempting to be diversified. In this paper, we investigate the challenge problem of product recommendation in unexplored categories and discover that the price, a factor transferrable across categories, can improve the recommendation performance significantly. Through our investigation, we address four research questions progressively: 1) what is the impact of unexplored category on recommendation performance? 2) How to represent the price factor from the recommendation point of view? 3) What does price factor across categories mean to recommendation? 4) How to utilize price factor across categories for recommendation in unexplored categories? Based on a series of experiments and analysis conducted on a dataset collected from a leading E-commerce website, we discover valuable findings for the above four questions: first, unexplored categories cause performance drop by 40% relatively for current recommendation systems; second, the price factor can be represented as either a quantity for a product or a distribution for a user to improve performance; third, consumer behavior with respect to price factor across categories is complicated and needs to be carefully modeled; finally and most importantly, we propose a new method which encodes the two perspectives of the price factor. The proposed method significantly improves the recommendation performance in unexplored categories over the state-of-the-art baseline systems and shortens the performance gap by 43% relatively.

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Cited By

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  • (2021)Incorporating Price into Recommendation with Graph Convolutional NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3091160(1-1)Online publication date: 2021
  • (2020)Price-aware Recommendation with Graph Convolutional Networks2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00019(133-144)Online publication date: Apr-2020
  • (2019)CROSSProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331191(515-524)Online publication date: 18-Jul-2019
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  1. Does product recommendation meet its waterloo in unexplored categories?: no, price comes to help

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      cover image ACM Conferences
      SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
      July 2014
      1330 pages
      ISBN:9781450322577
      DOI:10.1145/2600428
      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: 03 July 2014

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      Author Tags

      1. price
      2. product recommendation
      3. unexplored category

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      SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;
      Overall Acceptance Rate 792 of 3,983 submissions, 20%

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      Cited By

      View all
      • (2021)Incorporating Price into Recommendation with Graph Convolutional NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3091160(1-1)Online publication date: 2021
      • (2020)Price-aware Recommendation with Graph Convolutional Networks2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00019(133-144)Online publication date: Apr-2020
      • (2019)CROSSProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331191(515-524)Online publication date: 18-Jul-2019
      • (2019)Personalized Promotion Recommendation Through Consumer Experience Evolution ModelingFuzzy Techniques: Theory and Applications10.1007/978-3-030-21920-8_61(692-703)Online publication date: 11-Jun-2019
      • (2018)Purpose-Feature Relationship Mining from Online Reviews towards Purpose-Oriented RecommendationIEICE Transactions on Information and Systems10.1587/transinf.2017DAP0013E101.D:4(1021-1029)Online publication date: 2018
      • (2017)Personal-discount sensitivity prediction for mobile coupon conversion optimizationJournal of the Association for Information Science and Technology10.1002/asi.2383868:8(1940-1952)Online publication date: 1-Aug-2017
      • (2016)Boosting Recommendation in Unexplored Categories by User Price PreferenceACM Transactions on Information Systems10.1145/297857935:2(1-27)Online publication date: 10-Oct-2016
      • (2015)E-commerce Recommendation with Personalized PromotionProceedings of the 9th ACM Conference on Recommender Systems10.1145/2792838.2800178(219-226)Online publication date: 16-Sep-2015
      • (2015)Predicting customer purchase behavior in the e-commerce contextElectronic Commerce Research10.1007/s10660-015-9191-615:4(427-452)Online publication date: 1-Dec-2015

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