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A new approach for item ranking based on review scores reflecting temporal trust factor
conference contribution
posted on 2023-05-23, 08:22 authored by Saito, K, Kimura, M, Ohara, K, Motoda, HWe propose a new item-ranking method that is reliable and can efficiently identify high-quality items from among a set of items in a given category using their review-scores which were rated and posted by users. Typical ranking methods rely only on either the number of reviews or the average review score. Some of them discount outdated ratings by using a temporal-decay function to make a fair comparison between old and new items. The proposed method reflects trust levels by incorporating a trust discount factor into a temporal-decay function. We first define the MTDF (Multinomial with Trust Discount Factor) model for the review-score distribution of each item built from the observed review data. We then bring in the notion of z-score to accommodate the trust variance that comes from the number of reviews available, and propose a z-score version of MTDF model. Finally we demonstrate the effectiveness of the proposed method using the MovieLens dataset, showing that the proposed ranking method can derive more reasonable and trustable rankings, compared to two naive ranking methods and the pure z-score based ranking method.
History
Publication title
Lecture Notes in Computer Science 8393: Proceedings of the 7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP 2014)Volume
8393Editors
W Kennedy, N Agarwal, SJ YangPagination
161-168ISSN
0302-9743Department/School
School of Information and Communication TechnologyPublisher
SpringerPlace of publication
Heidelberg, GermanyEvent title
7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP 2014)Event Venue
Washington, DC, USADate of Event (Start Date)
2014-04-01Date of Event (End Date)
2014-04-04Rights statement
Copyright 2014 Springer International Publishing SwitzerlandRepository Status
- Restricted