Perbandingan Akurasi dalam Elisitasi Berbasis Peringkat dengan Minat Pengguna

Zen Munawar, Novianti Indah Putri, Yudi Herdiana, Rita Komalasari

Abstract


Panjangnya profil pengguna yang harus dikumpulkan oleh sistem pemberi rekomendasi menjadi salah satu masalah dalam menyelesaikan rancangan sistem pemberi rekomendasi. Terdapat dua persyaratan yang saling bertolak belakang, yaitu pertama sistem harus mengumpulkan cukup peringkat agar dapat mempelajari preferensi serta akurasi rekomendasi, kedua dengan mengumpulkan lebih banyak peringkat akan menjadi beban untuk pengguna dan berdampak negative atas pengalamannya. Tujuan penelitian ini berusaha mengetahui dampak dari panjangnya profil pengguna secara subjektif dan berbasis akurasi secara objektif. Dalam penelitian ini tiga algoritma diterapkan pada simulasi secara offline serta melakukan eksperimen secara online dengan empat algoritma pemberi rekomendasi. Dengan mengukur tingkat kekuatan dari dua kekuatan kontras yang dipengaruhi oleh jumlah peringkat yang dikumpulkan. Diperoleh relevansi rekomendasi dan beban proses peringkat yang memiliki efek yang lebih kuat pada kualitas yang dirasakan dari pengalaman pengguna. Hasil penelitian ini dilakukan pengendalian oleh pengguna serta mengidentifikasi panjang profil yang berpotensi optimal untuk strategi elisitasi yang eksplisit, berbasis peringkat.

Keywords


akurasi; elisitasi; peringkat; minat

Full Text:

PDF

References


S. Drenner, S. Sen, and L. Terveen, “Crafting The Initial User Experience to Achieve Community Goals,” in RecSys’08: Proceedings of the 2008 ACM Conference on Recommender Systems, 2008, no. May, pp. 187–194.

J. A. Konstan and J. Riedl, “Recommender systems: From algorithms to user experience,” User Model. User-adapt. Interact., vol. 22, no. 1–2, pp. 101–123, 2012.

A. M. Rashid, “Getting to Know You: Learning New User Preferences in Recommender Systems,” in 7th International Conference on Intelligent User Interfaces (IUI ’02), 2002, pp. 127–134.

Z. Munawar, “Penggunaan Profil Media Sosial Untuk Memprediksi Kepribadian,” Temat. - J. Teknol. Inf. dan Komun., vol. 4, no. 2 SE-Articles, Dec. 2017.

Z. Munawar, B. Siswoyo, and N. S. Herman, “Machine learning approach for analysis of social media,” ADRI Int. Journal. Information. Technol., vol. 1, pp. 5–8, 2017.

N. I. Munawar, Zen and Putri, “Keamanan Jaringan Komputer Pada Era Big Data,” J-SIKA| J. Sist. Inf. Karya Anak Bangsa, vol. 02, no. 01, pp. 14–20, 2020.

N. Golbandi, Y. Koren, R. Lempel, H. D. Management, and D. Applications, “On bootstrapping recommender systems-golbandi,” pp. 1805–1808, 2010.

S. Berkvosky, Y. Eytani, T. Kuflik, and F. Ricci, “Enhancing privacy and preserving accuracy of a distributed collaborative filtering,” in RecSys’07: Proceedings of the 2007 ACM Conference on Recommender Systems, 2007, pp. 97–104.

A. M. Rashid, G. Karypis, and J. Riedl, “Learning preferences of new users in recommender systems,” ACM SIGKDD Explor. Newsl., vol. 10, no. 2, pp. 90–100, 2008.

N. I. Putri, “Sistem pakar diagnosa tingkat kecanduan gadget pada remaja menggunakan metode Certainty Factor.” UIN Sunan Gunung Djati Bandung, 2018.

L. Chen and P. Pu, Interaction design guidelines on critiquing-based recommender systems, vol. 19, no. 3. Springer, 2009.

A. Pommeranz, J. Broekens, P. Wiggers, W. P. Brinkman, and C. M. Jonker, “Designing interfaces for explicit preference elicitation: A user-centered investigation of preference representation and elicitation process,” User Model. User-adapt. Interact., vol. 22, no. 4–5, pp. 357–397, 2012.

P. Pu, L. Chen, and R. Hu, “Evaluating recommender systems from the user’s perspective: Survey of the state of the art,” User Model. User-adapt. Interact., vol. 22, no. 4–5, pp. 317–355, 2012.

G. Häubl and V. Trifts, “Consumer decision making in online shopping environments: The effects of interactive decision aids,” Mark. Sci., vol. 19, no. 1, pp. 4–21, 2000.

N. Jones and P. Pu, “[cold start] User Technology Adoption Issues in Recommender Systems,” in Networking and Electronic Commerce Research Conference, 2007, no. January, pp. 379–394.

G. Lekakos and G. M. Giaglis, “A hybrid approach for improving predictive accuracy of collaborative filtering algorithms,” User Model. User-adapt. Interact., vol. 17, no. 1–2, pp. 5–40, 2007.

S. M McNee, J. A Konstan, J. Riedl, and S. K Lam, “Interfaces for Eliciting New User Preferences in Recommender Systems,” in User Modeling 2003, 9th International Conference, 2003, vol. 2702, no. June 2014, pp. 163–167.

K. Swearingen and R. Sinha, “Interaction design for recommender systems,” in Designing Interactive Systems, 2002, no. March 2002, pp. 1–10.

A. Adomavicius, G., Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” in IEEE Transactions on Knowledge and Data Engineering, 2005, vol. 17, no. 6, pp. 734–749.

R. Hu and P. Pu, “A comparative user study on rating vs. personality quiz based preference elicitation methods,” in International Conference on Intelligent User Interfaces, Proceedings IUI, 2009, pp. 367–371.

Z. Munawar, N. Suryana, Z. B. Sa’aya, and Y. Herdiana, “Framework With An Approach To The User As An Evaluation For The Recommender Systems,” in 2020 Fifth International Conference on Informatics and Computing (ICIC), 2020, pp. 1–5.

P. Cremonesi, Y. Koren, and R. Turrin, “Performance of recommender algorithms on top-N recommendation tasks,” in RecSys’10 - Proceedings of the 4th ACM Conference on Recommender Systems, 2010, pp. 39–46.

A. Bellogín, P. Castells, and I. Cantador, “Precision-oriented evaluation of recommender systems: An algorithmic comparison,” in RecSys’11 - Proceedings of the 5th ACM Conference on Recommender Systems, 2011, pp. 333–336.

P. Cremonesi, F. Garzotto, and R. Turrin, “Investigating the persuasion potential of recommender systems from a quality perspective: An empirical study,” ACM Trans. Interact. Intell. Syst., vol. 2, no. 2, pp. 1–41, 2012.

P. Lops, M. De Gemmis, and G. Semeraro, Recommender Systems Handbook. 2011.

R. Bambini, P. Cremonesi, and R. Turrin, Recommender Systems Handbook. 2011.

R. Hu and P. Pu, “Enhancing collaborative filtering systems with personality information,” in RecSys’11 - Proceedings of the 5th ACM Conference on Recommender Systems, 2011, pp. 197–204.

B. P. Knijnenburg, M. C. Willemsen, Z. Gantner, H. Soncu, and C. Newell, “Explaining the user experience of recommender systems,” User Model. User-adapt. Interact., vol. 22, no. 4–5, pp. 441–504, 2012.




DOI: https://doi.org/10.36080/bit.v18i1.1290

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Budi Luhur Information Technology

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

OFFICE:

FAKULTAS TEKNOLOGI INFORMASI - UNIVERSITAS BUDI LUHUR, Jl. Ciledug Raya, Petukangan Utara, Jakarta Selatan, 12260. DKI Jakarta, Indonesia. Telp: 021-585 3753 Fax: 021-585 3752

Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) by FAKULTAS TEKNOLOGI INFORMASI - UNIVERSITAS BUDI LUHUR is licensed under CC BY-SA 4.0 Creative Commons License

View Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Satats Web Analytics Made Easy