The Exploration of Restaurant Recommender System

Fahrudin, Tora; Wisna, Nelsi

doi:10.3844/jcssp.2022.784.791

Cited by 2 publications

(1 citation statement)

References 28 publications

(31 reference statements)

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“…streaming services [5], healthcare service recommendation [6], restaurant services [7]. This is because there is a lot of user's explicit and implicit data in many elds, such as social media data, wearable sensors, medical data, smart devices data, user's click data, user's behavior patterns, can be utilized to build recommendation systems.…”

Section: Introductionmentioning

confidence: 99%

New Custom Rating for Improving Recommendation System Performance

Fahrudin,

Wijaya

2024

Preprint

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Nowadays, the recommendation system still attracts many researchers. Various new businesses focusing on online marketing (E-Commerce) have emerged since the covid-19 pandemic. This phenomenon allows recommending items based on Collaborative Filtering Recommender System (CF) to enhance the shopping experience for users. An accurate CF depends on how similarity algorithms can find similar profile users correctly. The traditional similarity measures are based on the user-item rating matrix. In this paper, we employ four custom rating combine with new rating formula from the popularity of users, items, and its original rating which called called New Custom Rating (Ncr). Instead of focusing on original rating only, our Ncr tried to maximize the recommender system's performance by employing users' popularity and items to derive new ratings value. Using those formulas will make the new value rating more representative and the calculation of the similarity algorithm more precisely. Finally, the increased accuracy of the recommendation systems will be achieved. We conduct Ncr in four custom rating algorithms and four algorithms of the reccomender system in five public data sets. The experimental results demonstrate that Ncr can increase the recommendation system's accuracy by decreasing RMSE, MSE, and MAE and increasing FCP and Hit Rate. Ncr may improved accuracy of the reccomender system in various recommender systems algorithms by employing the popularity of users and items in rating.

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Section: Introductionmentioning

confidence: 99%

New Custom Rating for Improving Recommendation System Performance

Fahrudin,

Wijaya

2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

New custom rating for improving recommendation system performance

Fahrudin,

Wijaya

2024

J Big Data

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Recommendation system is currently attracting the interest of many explorers. Various new businesses have surfaced with the rise of online marketing (E-Commerce) in response to Covid-19 pandemic. This phenomenon allows recommendation items through a system called Collaborative Filtering (CF), aiming to improve shopping experience of users. Typically, the effectiveness of CF relies on the precise identification of similar profile users by similarity algorithms. Traditional similarity measures are based on the user-item rating matrix. Approximately, four custom ratings (CR) were used along with a new rating formula, termed New Custom Rating (NCR), derived from the popularity of users and items in addition to the original rating. Specifically, NCR optimized recommendation system performance by using the popularity of users and items to determine new ratings value, rather than solely relying on the original rating. Additionally, the formulas improved the representativeness of the new rating values and the accuracy of similarity algorithm calculations. Consequently, the increased accuracy of recommendation system was achieved. The implementation of NCR across four CR algorithms and recommendation system using five public datasets was examined. Consequently, the experimental results showed that NCR significantly increased recommendation system accuracy, as evidenced by reductions in RMSE, MSE, and MAE as well as increasing FCP and Hit Rate. Moreover, by combining the popularity of users and items into rating calculations, NCR improved the accuracy of various recommendation system algorithms reducing RMSE, MSE, and MAE up to 62.10%, 53.62%, 65.97%, respectively, while also increasing FCP and Hit Rate up to 11.89% and 31.42%, respectively.

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The Exploration of Restaurant Recommender System

Cited by 2 publications

References 28 publications

New Custom Rating for Improving Recommendation System Performance

New Custom Rating for Improving Recommendation System Performance

New custom rating for improving recommendation system performance

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