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Using Word2Vec recommendation for improved purchase prediction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Using Word2Vec recommendation for improved purchase prediction. / Esmeli, Ramazan; Bader-El-Den, Mohamed; Abdullahi, Hassana.

Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020. Institute of Electrical and Electronics Engineers, 2020. (2020 International Joint Conference on Neural Networks (IJCNN)).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Esmeli, R, Bader-El-Den, M & Abdullahi, H 2020, Using Word2Vec recommendation for improved purchase prediction. in Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020. 2020 International Joint Conference on Neural Networks (IJCNN), Institute of Electrical and Electronics Engineers, 2020 International Joint Conference on Neural Networks, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9206871

APA

Esmeli, R., Bader-El-Den, M., & Abdullahi, H. (2020). Using Word2Vec recommendation for improved purchase prediction. In Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020 (2020 International Joint Conference on Neural Networks (IJCNN)). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN48605.2020.9206871

Vancouver

Esmeli R, Bader-El-Den M, Abdullahi H. Using Word2Vec recommendation for improved purchase prediction. In Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020. Institute of Electrical and Electronics Engineers. 2020. (2020 International Joint Conference on Neural Networks (IJCNN)). https://doi.org/10.1109/IJCNN48605.2020.9206871

Author

Esmeli, Ramazan ; Bader-El-Den, Mohamed ; Abdullahi, Hassana. / Using Word2Vec recommendation for improved purchase prediction. Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020. Institute of Electrical and Electronics Engineers, 2020. (2020 International Joint Conference on Neural Networks (IJCNN)).

Bibtex

@inproceedings{b738c200eb4941a294158ad19eccbeb9,
title = "Using Word2Vec recommendation for improved purchase prediction",
abstract = "Purchase prediction can help e-commerce planners plan their stock and personalised offers. Word2Vec is a well-known method to explore word relations in sentences for sentiment analysing by creating vector representation of words. Word2Vec models are used in many works for product recommendations. In this paper, we analyse the effect of item similarities in the sessions in purchase prediction performance. We choose the items from different position of the session, and we derive recommendations from selected items using Word2Vec model. We assess the similarities between items by analysing the number of common recommendations of selected items. We train classification algorithms after we include similarity calculations of the selected items as session features. Computational experiments show that using similarity values of the interacted items in the session improves the performance of purchase prediction in terms of F1 score.",
keywords = "purchase intent, Word2vec, product recommendation, purchase behaviour prediction, browsing behaviour, classification, machine learning",
author = "Ramazan Esmeli and Mohamed Bader-El-Den and Hassana Abdullahi",
year = "2020",
month = sep,
day = "28",
doi = "10.1109/IJCNN48605.2020.9206871",
language = "English",
isbn = "978-1-7281-6927-9",
series = "2020 International Joint Conference on Neural Networks (IJCNN)",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020",
note = "2020 International Joint Conference on Neural Networks, IJCNN ; Conference date: 19-07-2020 Through 24-07-2020",

}

RIS

TY - GEN

T1 - Using Word2Vec recommendation for improved purchase prediction

AU - Esmeli, Ramazan

AU - Bader-El-Den, Mohamed

AU - Abdullahi, Hassana

PY - 2020/9/28

Y1 - 2020/9/28

N2 - Purchase prediction can help e-commerce planners plan their stock and personalised offers. Word2Vec is a well-known method to explore word relations in sentences for sentiment analysing by creating vector representation of words. Word2Vec models are used in many works for product recommendations. In this paper, we analyse the effect of item similarities in the sessions in purchase prediction performance. We choose the items from different position of the session, and we derive recommendations from selected items using Word2Vec model. We assess the similarities between items by analysing the number of common recommendations of selected items. We train classification algorithms after we include similarity calculations of the selected items as session features. Computational experiments show that using similarity values of the interacted items in the session improves the performance of purchase prediction in terms of F1 score.

AB - Purchase prediction can help e-commerce planners plan their stock and personalised offers. Word2Vec is a well-known method to explore word relations in sentences for sentiment analysing by creating vector representation of words. Word2Vec models are used in many works for product recommendations. In this paper, we analyse the effect of item similarities in the sessions in purchase prediction performance. We choose the items from different position of the session, and we derive recommendations from selected items using Word2Vec model. We assess the similarities between items by analysing the number of common recommendations of selected items. We train classification algorithms after we include similarity calculations of the selected items as session features. Computational experiments show that using similarity values of the interacted items in the session improves the performance of purchase prediction in terms of F1 score.

KW - purchase intent

KW - Word2vec

KW - product recommendation

KW - purchase behaviour prediction

KW - browsing behaviour

KW - classification

KW - machine learning

UR - https://wcci2020.org/

UR - https://ieeexplore.ieee.org/xpl/conhome/1000500/all-proceedings

U2 - 10.1109/IJCNN48605.2020.9206871

DO - 10.1109/IJCNN48605.2020.9206871

M3 - Conference contribution

SN - 978-1-7281-6927-9

T3 - 2020 International Joint Conference on Neural Networks (IJCNN)

BT - Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020

PB - Institute of Electrical and Electronics Engineers

T2 - 2020 International Joint Conference on Neural Networks

Y2 - 19 July 2020 through 24 July 2020

ER -

ID: 20955049