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Using Word2Vec recommendation for improved purchase prediction

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

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.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks (IJCNN) 2020
PublisherInstitute of Electrical and Electronics Engineers
Publication statusAccepted for publication - 17 May 2020
EventIEEE World Congress on Computational Intelligence (WCCI) 2020 - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

Name2020 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

ConferenceIEEE World Congress on Computational Intelligence (WCCI) 2020
CountryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20

Documents

  • word2vec

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    Accepted author manuscript (Post-print), 347 KB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 1/01/50

Related information

Relations Get citation (various referencing formats)

ID: 20955049