ATTRIBUTE WEIGHTING FOR MEDIA CONTENT-BASED RECOMMENDATION

摘要

#$%^&*AU2016247184A120170622.pdf#####Abstract (57) The invention is a computer-implemented method for generating content-based recommendations. A computer processor determines a first similarity score based on a statistical measure of similarity between user preferences for each of a first content item and a second content item. The computer processor determines a second similarity score based on a statistical measure of similarity between a first content attribute and a second content attribute. Training a predictive model by assigning, by the computer processor, a weight to the first content attribute based on the first and second similarity scores, and a weight to the second content attribute based on the first and second similarity scores. Generating, by the computer processor and using the predictive model, a content-based recommendation for a content item having both the first content attribute and the second content attribute based on the weights.300 ~ 302 Historical Rating Similarity Calculate a first similarity score Estimation Mdl 122 *304 Content Attribute 1 Similarity CCalc lateasecondsimilarityscore Calculation Module~ 124 306 Attribute Weight Assign weights to a first content Assignment attribute and a second content Module attribute 126 308 Content Calculate a third similarity score Recommendation and generate a content-based Module recommendation FIG. 3