Restaurant Recommender System Using User-Based Collaborative Filtering Approach: A Case Study at Bandung Raya Region

Fakhri, Alif Azhar; Baizal, Z. K. A.; Setiawan, Erwin Budi

doi:10.1088/1742-6596/1192/1/012023

Cited by 38 publications

(26 citation statements)

References 3 publications

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“…Beberapa dari seluruh hidangan yang ada dan barangkali disukai pelanggan dapat dipilih sekaligus direkomendasikan secara otomatis dengan memanfaatkan sistem rekomendasi [3]. Sistem rekomendasi dapat diartikan sebagai sebuah sistem yang menganalisis data produk dan pengguna untuk menemukan hubungan di antara keduanya dan kemudian hubungan tersebut ditampilkan dalam bentuk rekomendasi [4].…”

Section: Pendahuluanunclassified

“…Penerapan k-nearest neighbor [5], [11] untuk mengurangi terjadinya error dalam prediksi. Penggunaan atribut pengguna seperti usia dan gender menghasilkan akurasi sistem rekomendasi yang lebih rendah dibandingkan dengan yang tidak menggunakannya [3].…”

Section: Pendahuluanunclassified

“…Nilai support dari 2 karakteristik dan dihitung dengan Persamaan (2). Selain itu, nilai confidence dari rule A→B dihitung menggunakan Persamaan (3).…”

Section: Metode Content-based Filteringunclassified

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Sistem Rekomendasi Hybrid untuk Pemesanan Hidangan Berdasarkan Karakteristik dan Rating Hidangan

Tommy¹,

Novianto²,

Japriadi³

2020

JAIC

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The method that was often applied in recommender systems was content-based filtering or collaborative filtering which had several drawbacks if applied singly so that its accuracy was not too high. This study intended to solve the drawbacks of both by combining these two methods into a hybrid method. Apriori algorithm was used to provided recommendations based on dishes’s category and price range in customer order history or wishlist. The similarity between dishes was calculated using adjusted-cosine similarity algorithm while customer’s rating for dishes prediction was calculated using weighted sum algorithm. The values generated by these two methods were then averaged for recommendation process. The proposed hybrid recommender system successfully combines content-based with collaborative filtering methods where its precision and recall values when measured by confusion matrix are 80.73% and 76.52%. By considering the characteristics of dishes that have been ordered by customer, the recommender system is able to recommend new dishes or dishes that have not been ordered as long as their characteristics are similar to the dishes the customer has ordered.

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Section: Pendahuluanunclassified

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Sistem Rekomendasi Hybrid untuk Pemesanan Hidangan Berdasarkan Karakteristik dan Rating Hidangan

Tommy¹,

Novianto²,

Japriadi³

2020

JAIC

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“…Consumers, on the other hand, frequently find it time-consuming and difficult to extract meaningful information from the vast amount of online information available, making selecting a restaurant even more challenging. Giv-Volume 13, Number 4, 2022 en how people's lifestyles are changing as a result of their use of technology, an intelligent recommender system that recommends restaurants can be a good solution for consumers to assist them in finding a restaurant that fits their needs and preferences [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Trust-Based Recommender System for Personalized Restaurants Recommendation

Shambour

Abualhaj

Abu-Shareha

2022

Int. j. electr. comput. eng. syst. (Online)

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Several online restaurant applications, such as TripAdvisor and Yelp, provide potential consumers with reviews and ratings based on previous customers’ experiences. These reviews and ratings are considered the most important factors that determine the customer’s choice of restaurants. However, the selection of a restaurant among many unknown choices is still an arduous and time- consuming task, particularly for tourists and travellers. Recommender systems utilize the ratings provided by users to assist them in selecting the best option from many options based on their preferences. In this paper, we propose a trust-based recommendation model for helping consumers select suitable restaurants in accordance with their preferences. The proposed model utilizes multi- criteria ratings of restaurants and implicit trust relationships among consumers to produce personalized restaurant recommendations. The experimental results based on a real-world restaurant dataset demonstrated the superiority of the proposed model, in terms of prediction accuracy and coverage, in overcoming the sparsity and new user problems when compared to other baseline CF-based recommendation algorithms.

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“…Among these recommendation approaches, collaborative filtering is generally considered as one of the most used and most successful recommendation technologies in the recommendation system, especially e-commerce websites such as Amazon.com, Netflix and Google News [7]. In addition, collaborative filtering has also been applied to restaurant recommender system, which is useful for providing recommendations to users who will choose or buy a specific culinary menu based on the ratings given by other users [9]…”

Section: Introductionmentioning

confidence: 99%

Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity

Romadhon

Sediyono

Widodo

2020

KINETIK

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The Recommendation System plays an increasingly important role in our daily lives. With the increasing amount of information on the internet, the recommendation system can also solve problems caused by increasing information quickly. Collaborative filtering is one method in the recommendation system that makes recommendations by analyzing correlations between users.Collaborative filtering accumulates customer product ratings, identifies customers with common ratings, and offers recommendations based on inter-customer comparisons. It’s based on the idea that people who agree in their evaluations of certain items in the past are likely to agree again in the future. In this research, we analyze the differences in various implementation of collaborative filtering approaches such as item-based, item-based with the genre, and user-based.

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Restaurant Recommender System Using User-Based Collaborative Filtering Approach: A Case Study at Bandung Raya Region

Cited by 38 publications

References 3 publications

Sistem Rekomendasi Hybrid untuk Pemesanan Hidangan Berdasarkan Karakteristik dan Rating Hidangan

Sistem Rekomendasi Hybrid untuk Pemesanan Hidangan Berdasarkan Karakteristik dan Rating Hidangan

A Trust-Based Recommender System for Personalized Restaurants Recommendation

Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity

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