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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.

Group Ranking Sequence Decision for Recommendation of Messaging APP