Intelligent Offer Model for Game Online Item with Principle Hybrid Filtering Together with Categorical Data Technique

This paper focus to Intelligent offer pattern, The commonly used raw material, wearing: costumes, earrings, gloves, gloves, boots, shoes, costumes, hair, necklace, two hands, costume masks, hats, costumes, weapons, clothes, watches, rings, plaques and feature character. Robot and various tools are built to response of players. One of the expectations of most players is items selection which is selected from the User Interface. It is obtrusive noise of view screen while players select a few of the items. Object Items of games offer to each player overlap on the screen. It helps reduce event to switch the screen to maximize revenue from the purchase, sale, and exchange object items in the online game. we use Double Two dimension Principle Component Analysis for the feature extraction data behavior which is behavior a repeated in each player such as character of clothes, characteristic of choose activity , characteristic of use game items. We built Collaborative group pattern (CGP) and analysis player separate to each corporative group. Adding create offer game items in each corporative group. Each player will receive the best offers. The game operator has the opportunity to sell game items online as possible by without interrupting game play. The results of experiment showed that this method can offer a game item which is player want to sequence in the top of the list, and average 11.41 percentages is garbage items which player does not want, by game items which each player need to be in the order of 1-10 of menu