Distributed Recommendation Considering Aggregation Diversity

Distributed Recommendation Considering Aggregation Diversity

Na Zhao, Xu He
Copyright: © 2021 |Pages: 15
DOI: 10.4018/IJDST.2021070105
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Recommender systems (RSs) are popular in e-commerce as they suggest different kinds of items for different users. Most existing research works focus on how to improve the accuracy of recommender systems. Recently, some recommendation ranking techniques have been proposed to obtain more diverse recommendations for all the users. In this paper, the authors propose design a distributed mechanism for improving the aggregated recommendation diversity and define three new metrics to evaluate the diversity of RSs. To avoid the disclosure of information to a central agency, a distributed mechanism is designed to collect user ratings. To increase the diversity of set recommendations, user-based and item-based weighted methods are proposed. The tasks of them are to deal with non-ratings by weighting the common ratings and calculating the weighted cosine similarities to predict the unknown ratings.
Article Preview
Top

This section reviews the definition of the data model and recommended ranking methods. Table 1 lists the symbol systems in this paper.

Complete Article List

Search this Journal:
Reset
Volume 15: 1 Issue (2024)
Volume 14: 2 Issues (2023)
Volume 13: 8 Issues (2022)
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing