An Extended Collaborative Filtering-based Recommendation Procedure for Multimedia Contents in M-Commerce

As mobile market grows more and more fast, the mobile contents market, especially music contents for mobile phones have record remarkable growth. In spite of this rapid growth, mobile web users experience high levels of frustration to search the desired music. And new musics are very profitable to the content providers, but the existing CF system can’t recommend them. To solve this problem, we propose an extended CF system to reflect the user’s real preference by representing users in the feature space. We represent the musics using the music’s content based acoustic feature like timbral, MFCCs, rhythmic, and pitch contents in multi-dimensional feature space, and then select a neighborhood with distance based function. And for new music recommendation, we match the new music with other users’ preference. To verify the performance of the proposed system, the simulation imitating the real user’s decision-making and context in conducted. Through comparison with the pure CF, we validate our system’s performanc