A Books Recommendation Approach Based on Online Bookstore Data
This addresses book selection challenges for users and advertising needs for businesses, but appears incremental in combining existing methods.
The paper tackles book recommendation by identifying key factors influencing user ratings through expert interviews and AHP-fuzzy evaluation, then predicts user evaluations using nearest neighbor analysis on bookstore data.
In the era of information explosion, facing complex information, it is difficult for users to choose the information of interest, and businesses also need detailed information on ways to let the ad stand out. By this time, it is recommended that a good way. We firstly by using random interviews, simulations, asking experts, summarizes methods outlined the main factors affecting the scores of books that users drew. In order to further illustrate the impact of these factors, we also by combining the AHP consistency test, then fuzzy evaluation method, empowered each factor, influencing factors and the degree of influence come. For the second question, predict user evaluation of the listed books from the predict annex. First, given the books Annex labels, user data extraction scorebooks and mathematical analysis of data obtained from SPSS user preferences and then use software to nearest neighbor analysis to result in predicted value.