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RETRACTED ARTICLE: Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendations

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This article was retracted on 30 May 2022

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Abstract

These days users are able to save their time and effort by purchasing products online via various e-commerce websites. Their experience with a product exists in the form of textual reviews/feedbacks provided by them. Recommender systems offer personalized choices to users by capturing their interests and preferences. Through this paper identification of underlying topics using existing topic modeling techniques in user provided reviews of Moto e5 mobile on e-commerce website Amazon has been done and these techniques contrasted. Topic modeling is unsupervised learning technique used to identify hidden topics from a document (all the reviews of a product in this paper’s context). Coherence score, a measure of goodness of a topic reflecting the quality of human judgment compares these techniques. The higher the coherence score, the topic is more coherent. Experiments performed reveal that LDA technique performed better on the scrapped dataset.

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Authors and Affiliations

  1. J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India

    Dimple Chehal, Parul Gupta & Payal Gulati

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  1. Dimple Chehal
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  2. Parul Gupta
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  3. Payal Gulati
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Correspondence to Dimple Chehal.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-04004-7

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Chehal, D., Gupta, P. & Gulati, P. RETRACTED ARTICLE: Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendations. J Ambient Intell Human Comput 12, 5055–5070 (2021). https://doi.org/10.1007/s12652-020-01956-6

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  • DOI: https://doi.org/10.1007/s12652-020-01956-6

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