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Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data

Published: 16 September 2015 Publication History

Abstract

This paper describes some of the key properties of the proposed solution for the RecSys 2015 Challenge from the team Tøyvind thørrud. Three contributions will be highlighted: i) Feature extraction, ii) Classifier design, and iii) Decision rules to optimize the prediction results towards the RecSys Challenge's score. We finished sixth out of more than 250 active teams in the competition.

References

[1]
D. Ben-Shimon, A. Tsikinovsky, M. Friedmann, B. Shapira, L. Rokach, and J. Hoerle. Recsys challenge 2015 and the yoochoose dataset. In Proceedings of the 9th ACM conference on Recommender systems. ACM.
[2]
L. Breiman. Random forests. Mach. Learn., 45(1):5--32, Oct. 2001.

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  1. Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data

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    cover image ACM Conferences
    RecSys '15 Challenge: Proceedings of the 2015 International ACM Recommender Systems Challenge
    September 2015
    53 pages
    ISBN:9781450336659
    DOI:10.1145/2813448
    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: 16 September 2015

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    Author Tags

    1. Decision rules
    2. Feature extraction
    3. Random forest

    Qualifiers

    • Short-paper
    • Research
    • Refereed limited

    Conference

    RecSys '15
    Sponsor:
    RecSys '15: Ninth ACM Conference on Recommender Systems
    September 16 - 20, 2015
    Vienna, Austria

    Acceptance Rates

    RecSys '15 Challenge Paper Acceptance Rate 12 of 21 submissions, 57%;
    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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