Ensemble-based Top-k Recommender System Considering Incomplete Data

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

MORADI M.; HAMIDZADEH J.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2019 | Volume:7 | Issue:3 Page(s): 393-402

Download Full-Text

View:

189

Download:

120

Cites:

Information Journal Paper

Title

Ensemble-based Top-k Recommender System Considering Incomplete Data

Author(s)

MORADI M. | HAMIDZADEH J. | Issue Writer Certificate

Keywords

Top-k Recommender Systems

Incomplete Data

Ensemble Learning

Abstract

Recommender systems have been widely used in e-commerce applications. They are a sub-class of information filtering system used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be of user-interest (Top-k recommendation problem). Demanding sufficient ratings to make robust predictions and suggesting qualified recommendations are two significant challenges in recommender systems. However, the latter is far from satisfactory because human decisions are affected by environmental conditions, and they might change over time. In this paper, we introduce an innovative method to impute ratings to missed components of the rating matrix. We also design an ensemble-based method to obtain Top-k recommendations. In order to evaluate the performance of the proposed method, several experiments have been conducted based on 10-fold cross-validation over real-world datasets. The experimental results show that the proposed method is superior to the state-of-the-art competing methods regarding the applied evaluation metrics.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

MORADI, M., & HAMIDZADEH, J.. (2019). Ensemble-based Top-k Recommender System Considering Incomplete Data. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, 7(3), 393-402. SID. https://sid.ir/paper/723907/en

Vancouver: Copy

MORADI M., HAMIDZADEH J.. Ensemble-based Top-k Recommender System Considering Incomplete Data. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING[Internet]. 2019;7(3):393-402. Available from: https://sid.ir/paper/723907/en

IEEE: Copy

M. MORADI, and J. HAMIDZADEH, “Ensemble-based Top-k Recommender System Considering Incomplete Data,” JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, vol. 7, no. 3, pp. 393–402, 2019, [Online]. Available: https://sid.ir/paper/723907/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops