The Development of Mobile Culinary Recommendation System Based on Group Decision Support System

Dewi, Ratih Kartika; Ananta, Mahardeka Tri; Fanani, Lutfi; Brata, Komang Candra; Priandani, Nurizal Dwi

doi:10.3991/ijim.v12i3.7799

Cited by 13 publications

(7 citation statements)

References 4 publications

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“…The TOPSIS algorithm on [6] gave the culinary recommendation with the top ranking of the decision maker (DM), so DM with the highest vector value was chosen to have the first rank and the recommendation for this DM become the recommendation for groups. It was conducted by comparing each decision maker the alternative ranking value of as in Table 13.…”

Section: Results and Analysismentioning

confidence: 99%

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Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System

Dewi

2019

Jurnal Ilmu Komputer dan Informasi

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This paper proposes the integration of AHP and TOPSIS to generate the ranking results of culinary recommendation for a group of users to provide better recommendation results. Formerly, Group Decision Support System (GDSS) for culinary recommendations has been developed with the TOPSIS method. TOPSIS has low algorithm complexity, so it is suitable to be applied in mobile devices. However, GDSS with TOPSIS has its disadvantages, TOPSIS have not been able to facilitate the preferences of each user inside a group so the recommendation result always consist only on dominant user. TOPSIS method produces unchanging rankings, because this method recommends a food menu based on the 1 dominant user so that the ranking is always consistent. Meanwhile, this study aims to integrate AHP for weighting criteria from each user and TOPSIS for ranking culinary recommendations. Based on rank consistency testing results that conducted in 6 different user groups, unlike the previous research, AHP-TOPSIS shows inconsistency ranking, which means that changes in user preferences affect the recommendation results that are generated by application. The AHP-TOPSIS method proved can be accommodated the computation of various preferences of each user in GDSS culinary recommendation

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Section: Results and Analysismentioning

confidence: 99%

“…In 2017, Group Decision Support System for culinary recommendations with the TOPSIS method has been developed by Dewi [6]. TOPSIS has low algorithm complexity, so it is suitable to be applied in mobile devices.…”

Section: Jurnal Ilmu Komputer Dan Informasi (Journal Of Computer Sciementioning

confidence: 99%

Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System

Dewi

2019

Jurnal Ilmu Komputer dan Informasi

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“…For more than one user, the TOPSIS algorithm can be combined with the BORDA algorithm as an algorithm in the group DSS. Research on the Group DSS was also conducted previously on a mobile application [7] and culinary recommendations using TOPSIS [8] and AHP-TOPSIS [9].…”

Section: Introductionmentioning

confidence: 99%

“…But, there the TOPSIS-BORDA correlation testing hasn't been conducted yet. Mobile based group DSS with TOPSIS-BORDA [8] only discussed the usability testing of the tourism recommendation application, but there is no further study on its algorithm in detail. Therefore, this research contributes to testing the effectiveness of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

TOPSIS for mobile based group and personal decision support system

Dewi

Jonemaro

Kharisma

et al. 2021

regist. j. ilm. teknol. sist. inf.

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Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an algorithm that can be used for alternative design in a decision support system (DSS). TOPSIS provides recommendation so that users can get information that support their decision, for example a tourist wants to visit a tourist destination in Malang, then TOPSIS provides recommendations of tourist destinations in the form of ranking recommendation, with the highest rank is the most recommended recommendation. TOPSIS-based Mobile Decision Support System (DSS) has relatively low algorithm complexity. However, there are some cases that require development from personal DSS to group DSS, for example tourists rarely come alone, in which case most of them invite friends or family. For users who are more than 1 person, the TOPSIS algorithm can be combined with the BORDA algorithm. This study explains about the implementation & testing of TOPSIS and TOPSIS-BORDA as algorithms for personal and group DSS in mobile-based tourism recommendation system in Malang. Correlation testing was conducted to test the effectiveness of TOPSIS in mobile-based recommendation system. In previous study, correlation testing for personal DSS showed that there was a relationship between the recommendation and user choice, with correlation value of 0.770769231. In this study, correlation testing for group DSS showed there is a positive correlation of 0.88 between the recommendations of the group produced by TOPSIS-BORDA and personal recommendations for each user produced by TOPSIS.

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“…Technically, a recommender system can be categorized in various fields, such as machine learning, text classification, and information retrieval [1] [2]. The main aim of the RS is to find the relevant items by understanding users' behavior and predicting items that might be of interest to the user [3]. Recently, Collaborative Filtering (CF) has become one of the most common prototypes and most widely successful technologies in RS [4].…”

Section: Introductionmentioning

confidence: 99%

Two Models Based on Social Relations and SVD++ Method for Recommendation System

Sabaawi

Karacan

Yenice

2021

Int. J. Interact. Mob. Technol.

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Recently, Recommender Systems (RSs) have attracted many researchers whose goal is to improve the performance of the prediction accuracy of recommendation systems by alleviating RSs drawbacks. The most common limitations are sparsity and the cold-start problem. This article proposes two models to mitigate the effects of these limitations. The proposed models exploit five sources of information: rating information, which involves two sources, namely explicit and implicit, which can be extracted via users’ ratings, and two types of social relations: explicit and implicit relations, the last source is confidence values that are included in the first model only. The whole sources are combined into the Singular Value Decomposition plus (SVD++) method. First, to extract implicit relations, each non-friend pair of users, the Multi-Steps Resource Allocation (MSRA) method is adopted to compute the probability of being friends. If the probability has accepted value which exceeds a threshold, an implicit relationship will be created. Second, the similarity of explicit and implicit social relationships for each pair of users is computed. Regarding the first model, a confidence value between each pair of users is computed by dividing the number of common items by the total number of items which have also rated by the first user of this pair. The confidence values are combined with the similarity values to produce the weight factor. Furthermore, the weight factor, explicit, and implicit feedback information are integrated into the SVD++ method to compute the missing prediction values. Additionally, three standard datasets are utilized in this study, namely Last.Fm, Ciao, and FilmTrust, to evaluate our models. The experimental results have revealed that the proposed models outperformed state-of-the-art approaches in terms of accuracy.

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The Development of Mobile Culinary Recommendation System Based on Group Decision Support System

Cited by 13 publications

References 4 publications

Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System

Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System

TOPSIS for mobile based group and personal decision support system

Two Models Based on Social Relations and SVD++ Method for Recommendation System

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