Weighted Bayesian Rating Algorithm for Document Ranking

Author(s) : Dr. Krislan B. Ong

Volume & Issue : VOLUME 1 / 2016 , ISSUE 1

Page(s) : 1-4


Abstract

There have been many studies conducted in the area of information retrieval to solve the challenges brought about by information overload on the Web. One such technology that looks promising is collaborative filtering. This algorithm provides relevant search results or accurate recommendations to the specific needs of individuals. However, with the number of relevant information available on the Internet, it is also necessary to sort them base on the quality of the document. The proposed algorithm harnesses the collective intelligence of the crowd to determine the quality of the document from the explicit rating of the users. The main significance of the proposed algorithm is that it provides a scheme to assign appropriate weights in rating documents. The level of expertise of a user in a given topic is calculated and used as weights to the ratings. The level of expertise of a user is also used to influence the ranking of documents in the collection. The tags assigned to a document are calculated and used as weights to influence for the overall rating of the document. The proposed algorithm was evaluated and compared with other rating algorithms. The result proves that the proposed algorithm provides a better rating result compared to other algorithms. The proposed algorithm also provides a better representation of the wisdom of the crowd compared to other algorithms. Lastly, the study can also be used to improve the personalization of web search results and recommendation of documents in a collaborative filtering environment.



Keywords

Rating Algorithm

References

[1] A. Singhal. Modern information retrieval: a brief
overview. IEEE Data Engineering Bulletin,
Special Issue on Text and Databases, 24(4),
Dec. 2001.
[2] F. Liu, C. Yu, and W. Meng, “Personalized Web
Search for Improving Retrieval Effectiveness,”
IEEE Trans Knowledge and Data Engineering,
vol. 16, no. 1, 2004, pp. 28-40.
[3] Y.-H. Chien and E.I. George, “A Bayesian
Model for Collaborative Filtering,” Proc. Seventh
Int’l Workshop Artificial Intelligence and
Statistics, 1999.
[4] Memmel, M., Wolpers, M. &Tomadaki, E.
(2008).An Approach to Enable Collective
Intelligence in Digital Repositories.In
Proceedings of World Conference on Educational
Multimedia, Hypermedia and
Telecommunications 2008 (pp. 1803-1811).
Chesapeake, VA: AACE.