An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Ramzan, Bushra; Bajwa, Imran Sarwar; Jamil, Noreen; Amin, Riaz ul; Ramzan, Shabana; Mirza, Farhaan; Sarwar, Nadeem

doi:10.1155/2019/5941096

Cited by 65 publications

(26 citation statements)

References 39 publications

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“…Due to the growing diversity of data generated from hotels worldwide, a turn of attention has been observed in recent studies in adopting numerous ways of managing these valuable data. In [3], they used a big data solution involving Hadoop to deal with the variety of numeric data as well as textual data in the heterogeneous form. Sharma et al [12] used NLP (Natural Language Processing) in their work to determine the rating of the hotel used by the previous customers.…”

Section: Related Workmentioning

confidence: 99%

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An Effective Hotel Recommendation System through Processing Heterogeneous Data

et al. 2021

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Recommendation systems have recently gained a lot of popularity in various industries such as entertainment and tourism. They can act as filters of information by providing relevant suggestions to the users through processing heterogeneous data from different networks. Many travelers and tourists routinely rely on textual reviews, numerical ratings, and points of interest to select hotels in cities worldwide. To attract more customers, online hotel booking systems typically rank their hotels based on the recommendations from their customers. In this paper, we present a framework that can rank hotels by analyzing hotels’ customer reviews and nearby amenities. In addition, a framework is presented that combines the scores generated from user reviews and surrounding facilities. We perform experiments using datasets from online hotel booking platforms such as TripAdvisor and Booking to evaluate the effectiveness and applicability of the proposed framework. We first store the keywords extracted from reviews and assign weights to each considered unigram and bigram keywords and, then, we give a numerical score to each considered keyword. Finally, our proposed system aggregates the scores generated from the reviews and surrounding environments from different categories of the facilities. Experimental results confirm the effectiveness of the proposed recommendation framework.

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Section: Related Workmentioning

confidence: 99%

“…Customers' trust has become a crucial factor when making decisions for online hotel booking. There has been an increasing effort in the current state-of-the-art literature [1][2][3][4][5][6][7] to analyze hotel reviews and ratings in the last decade. In this paper, we build a framework to generate scores from hotel reviews and ratings.…”

Section: Introductionmentioning

confidence: 99%

An Effective Hotel Recommendation System through Processing Heterogeneous Data

et al. 2021

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“…Penelitian menggunakan analisa sentimen dalam pencarian hotel berdasarkan popularitas. Menghasilkan rekomendasi berkualitas tinggi dan mengurangi waktu dalam eksekusi sistem [5]. Dalam pemilihan kosentrasi mata kuliah dengan memakai data nilai mahasiswa dan data mata kuliah [6].…”

Section: Implementasiunclassified

Machine Learning Rekomendasi Produk dalam Penjualan Menggunakan Metode Item-Based Collaborative Filtering

Theodorus¹,

Defit

Nurcahyo

2021

jidt

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Industri 4.0 mendorong banyak perusahaan bertransformasi ke sistem digital. Machine Learning merupakan salah satu solusi dalam analisa data. Analisa data menjadi poin penting dalam memberikan layanan yang terbaik (user experience) kepada pelanggan. Lokasi yang diangkat dalam penelitian ini adalah PT. Sentral Tukang Indonesia yang bergerak dalam bidang penjualan bahan bangunan dan alat pertukangan seperti: cat, tripleks, aluminium, keramik, dan hpl. Dengan banyaknya data yang tersedia, menyebabkan perusahaan mengalami kesulitan dalam memberikan rekomendasi produk kepada pelanggan. Sistem rekomendasi muncul sebagai solusi dalam memberikan rekomendasi produk, berdasarkan interaksi antara pelanggan dengan pelanggan lainnya yang terdapat di dalam data histori penjualan. Tujuan dari penelitian ini adalah Membantu perusahaan dalam memberikan rekomendasi produk sehingga dapat meningkatkan penjualan, memudahkan pelanggan untuk menemukan produk yang dibutuhkan, dan meningkatkan layanan yang terbaik kepada pelanggan.Data yang digunakan adalah data histori penjualan dalam 1 periode (Q1 2021), data pelanggan, dan data produk pada PT. Sentral Tukang Indonesia. Data histori penjualan tersebut akan dibagi menjadi 80% untuk dataset training dan 20% untuk dataset testing. Metode Item-based Collaborative Filtering pada penelitian ini memakai algoritma Cosine Similarity untuk menghitung tingkat kemiripan antar produk. Prediksi score memakai rumus Weighted Sum dan dalam menghitung tingkat error memakai rumus Root Mean Squared Error. Hasil dari penelitian ini memperlihatkan rekomendasi top 10 produk per pelanggan. Produk yang tampil merupakan produk yang memiliki score tertinggi dari pelanggan tersebut. Penelitian ini dapat menjadi referensi dan acuan bagi perusahaan dalam memberikan rekomendasi produk yang dibutuhkan oleh pelanggan.

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“…Here they used the Fuzzy Cognitive Maps (FCMs) method to generate recommendations. Ramzan et al [39] analyses on heterogeneous data to identify suggestions. This proposed approach integrated lexical, syntax, and semantic analysis to understand user preferences.…”

Section: Other Rs Systemsmentioning

confidence: 99%

An Empirical Recommendation Framework to Support Location-Based Services

et al. 2020

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The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS). The need for providing suggestions to personals about the activities of their interests, the LBS contributing more effectively to this purpose. Recommendation system (RS) is one of the most effective and efficient features that has been initiated by the LBS. Our proposed system is intended to design a recommendation system that will provide suggestions to the user and also find a suitable place for a group of users and it is according to their preferred type of places. In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user’s and user-based Collaborative Filtering (CF) to find similar users as we are considering constructing an interest profile for each user. We also introduced a grid-based structure to present the Point of Interest (POI) into a map. Finally, similarity calculation is done to make the recommendations. We evaluated our system on real world users and acquired the F-measure score on average 0.962 and 0.964 for a single user and for a group of user respectively. We also observed that our system provides effective recommendations for a single user as well as for a group of users.

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An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Cited by 65 publications

References 39 publications

An Effective Hotel Recommendation System through Processing Heterogeneous Data

An Effective Hotel Recommendation System through Processing Heterogeneous Data

Machine Learning Rekomendasi Produk dalam Penjualan Menggunakan Metode Item-Based Collaborative Filtering

An Empirical Recommendation Framework to Support Location-Based Services

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