Whom to benefit? Competing platforms’ strategic investment in recommender systems

Liu, Yuan; Chen, Hongmin; Ruikun, Duan

doi:10.1016/j.elerap.2022.101210

Cited by 5 publications

(1 citation statement)

References 45 publications

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“…Implementing a personalized recommendation system is inseparable from the accompanying technology. Investment in a personalized recommendation system is proportional to the sophistication of the employed technology (Yuan et al , 2022). Therefore, we selected a relatively straightforward method that yielded reliable results.…”

Section: Research Implicationsmentioning

confidence: 99%

AsCDPR: a novel framework for ratings and personalized preference hotel recommendation using cross-domain and aspect-based features

Wang,

Justitia,

Wang

2023

DTA

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PurposeThe explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.Design/methodology/approachWe propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.FindingsExpanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.Research limitation/implicationsThis study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.Originality/valueThis study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

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Section: Research Implicationsmentioning

confidence: 99%