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Therefore, it is desired recommendation approach to be capable of handling the role dependencies and concept variations.
According to the Props priority-role dependency (P-RD) property, one player is only intent to buy the props desired most by his characters in game.
By minimizing the ranking error of multi-labels within an example (bag) in our MIML solution, the props priority-Role dependency can be easily formalized.
This is primarily because these data sets are with the nature of props priority-role dependency and concept variation, and then the assumptions made by our solutions are satisfied on these data sets.
Third, Props priority-Role Dependency (P-RD) There are lots of props in a certain game, while different types of characters, e.g., "warrior", "wizard", and "tactician", have different requirements for props.
We deal with game prop recommendation under the multi-instance multi-label learning framework directly against the complicated dependencies and long-distance interventions, and minimize the ranking error to meet the requirements of the props priority-role dependencies.
In this paper, we systematically analyze the specific natures of game props recommendation for the first time, and attribute the problems into four aspects: the complicated dependencies on contexts, long-distance interventions, props priority-role dependencies, and concept variations.
Aiming at the first two issues posted above, this section first gives our multi-instance multi-label representation for the game prop recommendation task in detail, and then formulates the game prop recommendation into a MIML learning problem together with figuring out the basic solution, which minimizes the ranking error according to props priority-role dependencies.
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