APPLICATION OF LEARNING TO RANK ON PERSIAN WEB CONTENT

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.

Keyhanipour Amir Hosein

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

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2019 | Issue:5

Download Full-Text

View:

139

Download:

Information Seminar Paper

Title

APPLICATION OF LEARNING TO RANK ON PERSIAN WEB CONTENT

Author(s)

Keyhanipour Amir Hosein

Keywords

LEARNING TO RANK

REINFORCEMENT LEARNING

PERSIAN WEB

DOTIR DATASET

Abstract

DATA ABOUT THE BEHAVIOR OF WEB USERS WHILE BROWSING THE RESULTS RETURNED BY WEB SEARCH ENGINES ENTITLED “ CLICK-THROUGH DATA” , IS A SOURCE OF IMPORTANT INFORMATION, WHICH IMPROVES THE FUNCTIONALITY OF WEB SEARCH ENGINES. MEANWHILE, ALMOST NONE OF THE RELEASED AND PUBLICLY AVAILABLE BENCHMARK DATASETS DEVOTED TO THE TASK OF LEARNING TO RANK, INCLUDE EXPLICIT CLICK-THROUGH DATA. AS A RESULT, MOST OF THE PROPOSED LEARNING TO RANK ALGORITHMS, IN PRACTICE HAVE NOT USED SUCH ENRICHED SOURCE OF DATA IN THEIR RANKING PROCESS. BESIDES, THESE DATASETS PRESENT A LARGE VOLUME OF FEATURES FOR THE TASK OF LEARNING TO RANK WHICH IN TURN BRINGS OUT NOTICEABLE COMPUTATIONAL COSTS FOR THE PROPOSED ALGORITHMS, AND MAKES THEM INEFFECTIVE IN REAL-WORLD SITUATIONS. TO ADDRESS THESE PROBLEMS, A NOVEL LEARNING TO RANK METHOD, QRC-RANK, HAS PROPOSED THE CONCEPT OF “ CLICK-THROUGH FEATURES” . IN THIS RESEARCH, THE FUNCTIONALITY OF THESE RANKING TECHNIQUES IS STUDIED IN THE RETRIEVAL OF THE PERSIAN CONTENTS BY APPLYING ON DOTIR BENCHMARK DATASET. THESE ANALYZES CLEARLY DEMONSTRATE SIGNIFICANT IMPROVEMENTS IN COMPARISON WITH WELL-KNOWN RANKING METHODS BASED ON THE EVALUATION CRITERIA. SPECIFICALLY, SUCH AN IMPROVEMENT IS MORE NOTICEABLE ON THE TOP OF RANKED LISTS, WHICH USUALLY ATTRACT USERS’ ATTENTIONS MORE THAN OTHER PARTS OF THESE LISTS.

Cites

No record.

References

No record.

Cite

APA: Copy

Keyhanipour, Amir Hosein. (2019). APPLICATION OF LEARNING TO RANK ON PERSIAN WEB CONTENT. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949101/en

Vancouver: Copy

Keyhanipour Amir Hosein. APPLICATION OF LEARNING TO RANK ON PERSIAN WEB CONTENT. 2019. Available from: https://sid.ir/paper/949101/en

IEEE: Copy

Amir Hosein Keyhanipour, “APPLICATION OF LEARNING TO RANK ON PERSIAN WEB CONTENT,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2019, [Online]. Available: https://sid.ir/paper/949101/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops