"game props" is a correct and usable phrase in written English.
Game props are objects used in a game to create a more immersive experience, such as costumes, tools, or pieces of a set. For example, "Players must gather the correct game props to win the game."
But the late hour did nothing to dampen the carnival atmosphere: fans had come armed with game props, and were in costume, or at least wearing T-shirts with official logos.
"Game props recommendation" is directed against the four issues discussed in this section, and introduces our game props recommendation solutions under the multi-instance multi-label framework.
Then, the game props recommendation task can be cast into an MIML prediction problem.
Thus, recommendation can be applied in phone game props sales similar to common goods transactions.
However, there are some prominent problems in directly applying the state-of-the-art recommendation methods to game props recommendation.
Second, Long-Distance Intervention (LDI) Player purchasing game props can be affected by events occurred long ago.
Open image in new window Fig. 2 Multi-instance multi-label representation for game prop recommendation task.
In game prop recommendation task, as time goes by, players will realize that some props are essential for their game characters, and become interested in buying these props.
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.
Therefore, by formulating game prop recommendation in MIML learning framework, the recommendation system can list multiple props for a player and the prediction values correspond to the degree of purchase intentions.
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.
