77 research outputs found
Social Influences in Recommendation Systems
Social networking sites such as Flickr and Facebook allow users to share
content with family, friends, and interest groups. Also, tags can often assign
to resources. In the previous research using few association rules FAR, we have
seen that high-quality and efficient association-based tag recommendation is
possible, but the set-up that we considered was very generic and did not take
social information into account. The proposed method in the previous paper,
FAR, in particular, exhibited a favorable trade-off between recommendation
quality and runtime. Unfortunately, recommendation quality is unlikely to be
optimal because the algorithms are not aware of any social information that may
be available. Two proposed approaches take a more social view on tag
recommendation regarding the issue: social contact variants and social groups
of interest. The user data is varied and used as a source of associations. The
adoption of social contact variants has two approaches. The first social
variant is User-centered Knowledge, to contrast Collective Knowledge. It
improves tag recommendation by grouping historic tag data according to friend
relationships and interests. The second variant is dubbed 'social batched
personomy' and attempts to address both quality and scalability issues by
processing queries in batches instead of individually, such as done in a
conventional personomy approach. For the social group of interest, 'community
batched personomy' is proposed to provide better accuracy groups of
recommendation systems in contrast also to Collective Knowledge. By taking
social information into account can enhance the performance of recommendation
systems.Comment: 6 page
Improving Performance of Relation Extraction Algorithm via Leveled Adversarial PCNN and Database Expansion
This study introduces database expansion using the Minimum Description Length
(MDL) algorithm to expand the database for better relation extraction.
Different from other previous relation extraction researches, our method
improves system performance by expanding data. The goal of database expansion,
together with a robust deep learning classifier, is to diminish wrong labels
due to the incomplete or not found nature of relation instances in the relation
database (e.g., Freebase). The study uses a deep learning method (Piecewise
Convolutional Neural Network or PCNN) as the base classifier of our proposed
approach: the leveled adversarial attention neural networks (LATTADV-ATT). In
the database expansion process, the semantic entity identification is used to
enlarge new instances using the most similar itemsets of the most common
patterns of the data to get its pairs of entities. About the deep learning
method, the use of attention of selective sentences in PCNN can reduce noisy
sentences. Also, the use of adversarial perturbation training is useful to
improve the robustness of system performance. The performance even further is
improved using a combination of leveled strategy and database expansion. There
are two issues: 1) database expansion method: rule generation by allowing step
sizes on selected strong semantic of most similar itemsets with aims to find
entity pair for generating instances, 2) a better classifier model for relation
extraction. Experimental result has shown that the use of the database
expansion is beneficial. The MDL database expansion helps improvements in all
methods compared to the unexpanded method. The LATTADV-ATT performs as a good
classifier with high precision P@100=0.842 (at no expansion). It is even better
while implemented on the expansion data with P@100=0.891 (at expansion factor
k=7).Comment: 6 page
Teknik Perangkingan Meta-search Engine
Meta-search engine mengorganisasikan penyatuan hasil dari berbagai search engine dengan tujuan untuk meningkatkan presisi hasil pencarian dokumen web. Pada survei teknik perangkingan meta-search engine ini akan didiskusikan isu-isu pra-pemrosesan, rangking, dan berbagai teknik penggabungan hasil pencarian dari search engine yang berbeda-beda (multi-kombinasi). Isu-isu implementasi penggabungan 2 search engine dan 3 search engine juga menjadi sorotan. Pada makalah ini juga dibahas arahan penelitian di masa yang akan datang
Metric Untuk Mengevaluasi Software Berdasarkan Pada Hasil-hasil Eksekusi Kasus
Artikel ini menjelaskan beberapa metrik yang digunakan untuk perangkat lunak dievaluasi dikembangkan oleh vendor. umum, adabeberapa Rason mengapa beberapa organisasi memutuskan untuk menggunakan vendor eksternal untuk mengembangkanperangkat lunak merek
SIKLUS PEMBANGUNAN APLIKASI JARINGAN SARAF TIRUAN
Untuk mengembangkan aplikasi jaringan syaraf tiruan, beberapa fase sistematis harus dilakukan. fase-fase sistematis ini dikenal sebagai siklus pengembangan jaringan syaraf tiruan. artikel ini menjelaskan langkah-langkah detail dari setiap fase dari siklusempat: tahap konsep tahap, tahap desain, tahap implementasi, dan pemeliharaa
METRIC UNTUK MENGEVALUASI SOFTWARE BERDASARKAN PADA HASIL-HASIL EKSEKUSI KASUS
Artikel ini menjelaskan beberapa metrik yang digunakan untuk perangkat lunak dievaluasi dikembangkan oleh vendor. umum, adabeberapa Rason mengapa beberapa organisasi memutuskan untuk menggunakan vendor eksternal untuk mengembangkanperangkat lunak merek
Highly Relevant Routing Recommendation Systems for Handling Few Data Using MDL Principle and Embedded Relevance Boosting Factors
A route recommendation system can provide better recommendation if it also
takes collected user reviews into account, e.g. places that generally get
positive reviews may be preferred. However, to classify sentiment, many
classification algorithms existing today suffer in handling small data items
such as short written reviews. In this paper we propose a model for a strongly
relevant route recommendation system that is based on an MDL-based (Minimum
Description Length) sentiment classification and show that such a system is
capable of handling small data items (short user reviews). Another highlight of
the model is the inclusion of a set of boosting factors in the relevance
calculation to improve the relevance in any recommendation system that
implements the model.Comment: ACM SIGIR 2018 Workshop on Learning from Limited or Noisy Data for
Information Retrieval (LND4IR'18), July 12, 2018, Ann Arbor, Michigan, USA, 8
pages, 9 figure
PERBANDINGAN ALGORITMA KRUSKAL DENGAN ALGORITMA GENETIKA DALAM PENYELESAIAN MASALAH MINIMUM SPANNING TREE (MST)
This research aims to develop a problem solving system of Minimum Spanning Tree using Kruskal Algorithm and Genetic Algorithm. The problem of Minimum Spanning Tree is how to calculate minimum distance in a complete graph where each nodes are connected and the selected edge should not make sircuit. This application system is built using Visual Basic 6.0 programming and MySQL database. The result of the whole process of this Minimum Spanning Tree aplication system is minimum distance which is calculated using Kruskal Algorithm and Gentic Algorithm. The displayed result are both text and visualization graph which show the minimum tree of a graph. Kruskal Algorithm generally shows better result than Genetic Algorithm with minimum distance resulted and running time as the parameters. For 5-25 nodes, Kruskal Algorithm generate minimum distance up to 50% and running time process 50 times faster than Genetic Algorithm
Sistem Informasi Geografis Pemetaan Faktor-faktor Yang Mempengaruhi Angka Kematian Ibu (Aki) Dan Angka Kematian Bayi (Akb) Dengan Metode K-means Clustering (Studi Kasus: Provinsi Bengkulu)
Angka Kematian Ibu (AKI) dan Angka Kematian Bayi (AKB) merupakan salah satu indikator penting dalam menilai tingkat derajat kesehatan masyarakat di suatu negara. Berdasarkan data Dinas Kesehatan Provinsi Bengkulu tahun 2012 hingga 2015, AKI dan AKB di Provinsi Bengkulu masih diatas rata-rata nasional. K-Means Clustering merupakan salah satu metode pengelompokan non hirarki yang bertujuan mengelompokkan objek sedemikian hingga jarak-jarak tiap objek ke pusat kelompok di dalam satu kelompok adalah minimum. Penelitian ini bertujuan (1) Merancang dan membangun Sistem Informasi Geografis untuk memetakan angka kematian ibu dan bayi di setiap Kota/Kabupaten di Provinsi Bengkulu menggunakan metode K-Means Clustering, (2) Mengetahui perbedaan dan status pengelompokkan angka kematian ibu dan bayi di setiap Kota/Kabupaten di Provinsi Bengkulu. Hasil penelitian yang diperoleh yaitu (1) Penelitian ini berhasil memetakan angka kematian ibu dan bayi dalam 3 kelompok, yaitu rendah, sedang dan tinggi (2) berhasil menerapkan metode K-Means Clustering (3) Persentasi AKI berdasarkan kota/kabupaten di Provinsi Bengkulu, sebagai berikut: 15% kota/kabupaten berada di tingkat rendah, 65% berada di tingkat sedang dan 20% berada di tingkat tinggi. Sedangkan persentasi AKB-nya 32,5% kota/kabupaten berada di tingkat rendah, 60% berada di tingkat sedang dan 7,5% berada di tingkat tinggi. Secara keseluruhan dapat dikatakan bahwa tingkat AKI/AKB di Provinsi Bengkulu masih belum memuaskan, yaitu < 15% AKI dan < 32,5% AKB.
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