An Expert Recommendation System for Design Decision Making: Who Should be Involved in Making a Design Decision?

Bhat, M. Seetharama; Shumaiev, Klym; Koch, Kevin M.; Hohenstein, Uwe; Biesdorf, Andreas; Matthes, Florian

doi:10.1109/icsa.2018.00018

Cited by 14 publications

(4 citation statements)

References 38 publications

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“…Finding an expert for CQA is a challenging problem that appeared in many applications, like routing the questions and nding the best answers [15]. e recommendation system comprises the technologies that can aid in nding the appropriate experts for the users [16]. A naïve model representation is shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Usman

Ahmad

Habib

et al. 2022

Computational Intelligence and Neuroscience

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Community Question Answering (CQA) web service provides a platform for people to share knowledge. Quora, Stack Overflow, and Yahoo! Answers are few sites where questioners post their queries and answerers respond to their respective queries. Due to the ease of use and quick responsiveness of the CQA platform, these sites are being widely adopted by the community. For better usability, there is a dire need to route the question toward the relevant answerers. To fulfil this gap, recommender systems play an important role in identifying the relevant answerers. To map the user interests more effectively, this research work proposed a dynamic feature representation of the latent user attributes for user profiling. The latent features are mapped by leveraging the Latent Dirichlet Allocation (LDA) for topic modelling of user data. The proposed recommendation model segments the user profile based on these latent user profiles incorporating the incremental learning of the users’ interests to produce the relevant recommendations in near real time. The experimental setup generated recommendation lists of variable sizes and evaluated using multiple evaluation metrics, such as mean average precision, recall, throughput, and different quality metrics, such as discounted cumulative gain and mean reciprocal rank. The results showed that the proposed model provided a better quality of recommendations in CQA forums, which is promising for future research in this domain.

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Section: Introductionmentioning

confidence: 99%

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Usman

Ahmad

Habib

et al. 2022

Computational Intelligence and Neuroscience

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“…Within several branches of recommendation research, our task is by concept similar to expert recommendation for question answering Nikzad-Khasmakhi et al, 2019). In this field, many previous studies encode expertise knowledge in diverse streams, such as software engineering (Bhat et al, 2018), social activities (Bok et al, 2021), etc. Nevertheless, few of them attempt to model expertise with NLP methods.…”

Section: Related Workmentioning

confidence: 99%

Doctor Recommendation in Online Health Forums via Expertise Learning

Lu¹,

Zhang²,

Li³

et al. 2022

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Huge volumes of patient queries are daily generated on online health forums, rendering manual doctor allocation a labor-intensive task. To better help patients, this paper studies a novel task of doctor recommendation to enable automatic pairing of a patient to a doctor with relevant expertise. While most prior work in recommendation focuses on modeling target users from their past behavior, we can only rely on limited words in a query to infer a patient's needs for privacy reasons. For doctor modeling, we study the joint effects of their profiles and previous dialogues with other patients and explore their interactions via selflearning. The learned doctor embeddings are further employed to estimate their capabilities of handling a patient query with a multihead attention mechanism. For experiments, a large-scale dataset is collected from Chunyu Yisheng, a Chinese online health forum, where our model exhibits state-of-the-art results, outperforming baselines only considering profiles and past dialogues to characterize a doctor. 1

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“…dengan menawarkan cara yang lebih cerdas dan dipersonalisasi bagi pengguna untuk mendapatkan informasi baru lebih cepat dan mudah (Kalaivanan & Vengatesan, 2013). Untuk membantu para pemangku kepentingan menyelesaikan permasalahan besar dalam pekerjaan maka perlu dirancang sistem perangkat lunak (software system) dan sistem rekomendasi dalam rekayas perangkat lunak dikembangkan (Bhat et al, 2018). Umumnya, sistem rekomendasi telah banyak digunakan dalam pengambilan keputusan pada berbagai program, seperti keputusan seleksi penerima beasiswa dengan metode Analitical Hierarchy Process (AHP) dan Technique for Order Preference by Similarity to Ideal Solution TOPSIS (Risnasari & Cahyani, 2018).…”

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“…Keterlambatan penyaluran bantuan sosial sangat mungkin terjadi karena sistem seleksi penerima dilakukan secara manual, dan sangat memungkinkan terjadi kesalahan dalam menentukan kelayakan penerima. Oleh karena itu, diperlukan sebuah sistem yang dapat menentukan penerima atau rekomendasi untuk membuat keputusan (Bhat et al, 2018), dimana sistem rekomendasi yang telah digunakan dalam beberapa studi sebelumnya seperti Re-rengking model (Kalaivanan & Vengatesan, 2013;Rosele & Ramli, 2012), Simple Additive Weightening (Susanto, Puspaningrum, & Neneng, 2021), metode AHP dan TOPSIS (Risnasari & Cahyani, 2018) dan juga model Z-Score (Basri, 2018), sehingga sistem pemeringkatan dapat mempercepat proses penentuan penerima bantuan sosial maupun zakat secara akurat dan lebih berhak. Studi sebelumnya menyatakan bahwa metode Standard Score (Z-Score merupakan teknik pemeringkatan yang lebih baik, disebabkan dapat membandingkan kualitas pencapaian hasil dengan rata-rata distribusi secara kelompok berdasarkan standar deviasi.…”

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Sistem Rekomendasi Penerima Bantuan Zakat Padabaitul Mal Kota Lhokseumawe Menggunakan Metode Z-Score

Risawandi,

Fadlisyah

2023

TTS 4.0

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Zakat memiliki peranan yang sangat strategis dalam upaya pengentasan kemiskinan atau pembangunan ekonomi. Umumnya pengelolaan memungut dan kemudian disalurkan zakat berdasarkan ketentuan yang telah ditetapkan kepada mustahiq dan manfaat hanyalah sebatas manfaat jangka pendek. Akan tetapi jika pengelolaan zakat digunakan untuk memberdayakan ekonomi mustahiq, maka akan mem berikan dampak atau manfaat jangka panjang bagi mustahiq. Sistem rekomendasi memiliki berbagai sifat yang dapat mempengaruhi pengalaman pengguna, seperti akurasi, ketahanan, skalabilitas, dan sebagainya. Dalam makalah ini kita membahas bagaimana membandingkan merekomendasikan beerdasarkan satu set properti yang relevan untuk aplikasi. Z-Score adalah suatu ukuran penyimpangan data dari nilai rata-ratanya yang diukur dalam satuan standar deviasinya. Jika nilainya terletak diatas rata-rata maka Z score-nya akan bernilai positif, sedangkan apabila nilainya dibawah nilai rata-rata maka Z score-nya akan bernilai negatif. Z-Score ini juga disebut dengan Nilai Standar atau Nilai Baku. Penelitian ini menggunakan lima kriteria dari mustahiq yang mendapatkan dana zakat, antara lain adalah umur, jenis kelamin, pekerjaan, tanggungan dan penghasilan. Dari 100 orang yang menjadi responden, didapat 27 orang yang layak menerima bantuan yang terdiri dari 26 perempuan dan 1 laki-laki.Kata kunci: Rekomendasi, Z-Score, Mean, Standard Deviasi, Penerima Bantuan

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An Expert Recommendation System for Design Decision Making: Who Should be Involved in Making a Design Decision?

Cited by 14 publications

References 38 publications

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Doctor Recommendation in Online Health Forums via Expertise Learning

Sistem Rekomendasi Penerima Bantuan Zakat Padabaitul Mal Kota Lhokseumawe Menggunakan Metode Z-Score

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