Implementation of multi-criteria collaborative filtering on cluster using Apache Spark

Wijayanto, Ardhi; Winarko, Edi

doi:10.1109/icstc.2016.7877370

Cited by 10 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Sistem yang tidak terskalakan (unscalable) memengaruhi kualitas layanan dan biaya pemeliharaan (Wijayanto, 2016). Sistem perlu diuji dengan baik untuk mengatasi penambahan beban agar tidak mengganggu kinerja bisnis.…”

Section: Pendahuluanunclassified

Analisis Skalabilitas Sistem Teknologi Informasi Dan Komunikasi: Kasus Ketertundaan Transformasi Dan Potensi Penerapan Continuous Auditing

Nay¹

2020

ABIS

View full text Add to dashboard Cite

AbstrakTujuan – Penelitian ini menganalisis skalabilitas sistem teknologi informasi dan komunikasi (TIK) Universitas Terbuka (UT), menganalisis penyebab UT belum mampu mencapai transformasi proses bisnis pada level yang tinggi, dan mengetahui potensi penerapan audit berkelanjutan (CA).Metode penelitian – studi kasus digunakan dalam penelitian ini. Data bersumber dari data primer dan data sekunder. Teknik pengumpulan data menggunakan studi dokumentasi, observasi, dan wawancara. Peneliti menggunakan teknik analisis data model Miles dan Huberman (2014), yang terdiri dari reduksi data, penyajian data, dan penarikan kesimpulan. Selanjutnya peneliti melakukan triangulasi dan member checking. Temuan – Hasil penelitian mengungkapkan sistem TIK UT tidak cukup terskalakan (unscalable) untuk menangani berbagai penambahan beban. Terdapat beragam fitur permintaan pengguna, namun TIK UT belum sanggup memenuhi kebutuhan tersebut. Terdapat ketimpangan antara strategi dengan kapabilitas yang dimiliki. Selain itu sistem TIK UT saat ini baru sebagian saja yang terotomatisasi dan terintegrasi sehingga audit berkelanjutan belum dapat diterapkan di UT.Originalitas – Penelitian-penelitian terdahulu banyak mengkaji hubungan antara penggunaan TIK dengan kinerja organisasi. Penelitian ini akan dititikberatkan pada pengujian skalabilitas sistem TIK; kasus ketertundaan transformasi dan pengidentifikasian potensi penerapan CA di UT.

show abstract

Section: Pendahuluanunclassified

Analisis Skalabilitas Sistem Teknologi Informasi Dan Komunikasi: Kasus Ketertundaan Transformasi Dan Potensi Penerapan Continuous Auditing

Nay¹

2020

ABIS

View full text Add to dashboard Cite

show abstract

“…Panigrahi et al used Alternating Least Square (ALS) on Spark and -means to avoid the data sparsity and scalability of collaborative filtering algorithms [43]. Wijayanto and Winarko implemented multicriteria collaborative filtering using Spark framework [44]. The experiments' results showed that efficiency of algorithms improved with the number of nodes in Spark clusters.…”

Section: Big Data Analytics For Recommendationmentioning

confidence: 99%

A Sentiment‐Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

Wang

2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.

show abstract

“…Experiments show that the accuracy of the recommended results has improved compared with the original algorithm. In [7], the multi-standard collaborative filtering algorithm extended horizontally and ran by adding the computing nodes. The experimental results show that the algorithm has not been improved obviously with the increase of nodes.…”

Section: Introductionmentioning

confidence: 99%

Spark-based Parallel Collaborative Filtering Recommendation Algorithm

Yang¹,

Xue²,

Cai³

et al. 2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

View full text Add to dashboard Cite

Abstract. The rapid development of Internet information technology makes the problem of information overload become more and more serious, and recommendation system is one of the effective ways to solve this problem which is favored by people. However, for the massive data information, the recommended algorithm faces the bottleneck problem of processing speed and computing resources, so this paper proposed a parallel collaborative filtering recommendation algorithm based on Spark. The RLPSO algorithm is used to optimize the clustering factor of the K-means clustering algorithm by associating users with similar interests into a cluster and using the recommended algorithm for users to recommend is implemented on the Spark platform. The experimental results show that the improved algorithm has a significant improvement in the prediction accuracy, and has a higher speedup and stability compared with the traditional collaborative filtering recommendation algorithm.

show abstract

Implementation of multi-criteria collaborative filtering on cluster using Apache Spark

Cited by 10 publications

References 7 publications

Analisis Skalabilitas Sistem Teknologi Informasi Dan Komunikasi: Kasus Ketertundaan Transformasi Dan Potensi Penerapan Continuous Auditing

Analisis Skalabilitas Sistem Teknologi Informasi Dan Komunikasi: Kasus Ketertundaan Transformasi Dan Potensi Penerapan Continuous Auditing

A Sentiment‐Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

Spark-based Parallel Collaborative Filtering Recommendation Algorithm

Contact Info

Product

Resources

About