Patent ID: 9189802
Filing Date: 2015-11-17
Classification: G06Q

Abstract:
1. A method for contents recommendation in an online contents store comprising steps for: providing a plurality of playlist's distributions associated with a plurality of users of the online contents store on an Internet respectively; selecting a novice user from the plurality of users, wherein the novice user has one or more novice-loved-contents in a long tail of an associated playlist's distribution as a novice-playlist's distribution; finding a first expert user with an associated playlist's distribution as a first-expert-playlist's distribution, wherein a short head of the first-expert-playlist's distribution includes at least one of the novice-loved-contents; finding a second expert user with an associated playlist's distribution as a second-expert-playlist's distribution, wherein a short head of the second-expert-playlist's distribution includes at least one of the novice-loved-contents; finding a third expert user with an associated playlist's distribution as a third-expert-playlist's distribution, wherein a short head of the third-expert-playlist's distribution includes at least one of the novice-loved-contents; assigning a weight for each of contents in the short head of each of the experts, wherein the weight depends on a sum of importances of the experts for the specific contents, wherein an importance of an expert is determined by a number of identical contents between the expert's playlist and each of the other expert's playlist and a number of identical loved-contents between the expert's playlist and each of the other expert's playlist; and recommending a first N highly-weighted contents to the novice user through the online contents store on the Internet, wherein the contents are songs, and wherein the importance of an exert, E where Ind (E j ) is the independent weight of the expert, N exp is the number of total experts, Imp (E i ) is the importance of Expert E i , and, Imp (E i ), i=1 to N exp , are calculated recursively, RegSongs (E j , E i ) denotes the number of same song occurrences in the playlists of experts E i and E j , N loved is the total number of ‘loved’ songs in the novice's long tail, and LovedSongs (E j , E i ) denotes the number of ‘loved’ songs that both experts share.