Recommender System for the Hobby-Activity-Item Rental Platform using User-based Collaborative Filtering

Hwang, Se-Won; Kim, ﻿Dayeong; Kim, Eun-Bae; Lee, Ye-Eun; Eom, Ye-Sol; Lee, Juyoung

doi:10.9728/dcs.2022.23.6.985

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Content Filtering and Collaborative Filtering1

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2023

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“…The proposed model was evaluated on a standard dataset and showed improved performance over state-of-the-art models. Hwang et al [23] proposed a recommendation system on a hobby item rental platform using collaborative filtering. Users may receive a recommendation only if they have a history of inquiring about an item.…”

Section: Content Filtering and Collaborative Filteringmentioning

confidence: 99%

Recommending K-Wave Items Tailored for Small-Sized Exporters by Incorporating Dense and Sparse Vectors

Lee,

Na,

Han

et al. 2023

Sustainability

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As K-wave has been strengthened via recent K-contents, K-wave items such as cosmetics and electronic devices have also gained attention globally. For small-sized export sellers who purchased the items and exported them to different countries, it is significant to discover which K-wave items are trending in specific countries. To do so, we proposed an ensemble recommender system by producing the dense vector, which is generated by a variant of Bidirectional Encoder Representations from Transformers (BERT), and balancing the vector with a sparse vector in order to ensure the efficient execution speed and recommendation accuracy. Based on the data we have collected specifically for potential K-items, our experiment showed that the proposed model outperforms the various baselines, which are used for content-based filtering.

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Section: Content Filtering and Collaborative Filteringmentioning

confidence: 99%