A Novel Recommendation Strategy for User-based Collaborative Filtering in Intelligent Marketing
Jing YI, Liang ZHANG, Phelan, C.A
Journal of Digital Information Management, Vol. 14, No. 2, 2016.
DOI: 
Link: 
Abstract:
Collaborative filtering (CF) is the most successful
and widely utilized recommendation technology.
CF-based recommenders perform well in terms of accuracy,
but they lack the capability to find fresh and novel
items. To improve the novel recommendation of userbased
CF, the definition of a novel item was established,
and an appropriate strategy of novel recommendation was
determined. First, a novel item containing the three aspects
of likability, unknown, and dissimilarity was defined
based on the meaning of the term novel. Second, metrics
that measure the novelty of the recommendation system
were designed based on the timeliness of novelty. Finally,
for the comparison of different strategies of novel recommendation,
unknown and dissimilarity were integrated into
the process and output of traditional algorithms. Results
show that the novelty of the recommendation system is
significantly improved when unknown and dissimilarity are
integrated into the recommendation results of the traditional
algorithm to recalculate the novelty of the item and
set the accuracy threshold. Output integration strategy
can improve the novelty of the recommended results and
can be utilized for any algorithm.