This research is to develop a novel
recommendation service using a unique group ranking sequence technique “Mining
Maximum Consensus Sequences from all Users’ Partial Ranking Lists (MCSP)”. MCSP
is capable of determining the product’s sequence recommendations based on
k-item candidate sequences and maximum consensus sequences. This paper also
illustrates the complete decision procedures of group ranking sequences. In
terms of popular information products, we select “messaging app” to reveal the
MCSP’s group ranking sequence decision. The recommendation service provides
that query users search for the product’s recommendation (i.e., messaging app)
according to the preference sequences from query users themselves and a great
deal of preference sequences from the other users. This paper consists of the
definitions, procedures, implementation, and experiment analysis, as well as
system demonstrations of MCSP respectively. This research contributes to a kind
of systematic service innovation.
Cite this paper
Tung, W. (2014) Group Ranking Sequence Decision for Recommendation of Messaging APP. Open Journal of Social Sciences
, 5-11. doi: 10.4236/jss.2014.27002
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