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Hi Yu,

Thank you for your time for viewing my paper.

For the background part, I have already inserted corresponding pictures to illustrate the meaning.

For the RMSE, I previously had the wrong result because during the coding process, I miscoded the column and row for their sparse matrix calculation so that the respective row are multiplied with respective row and resulted the wrong answer. However, I have already debugged it and rerun the experiement. The RMSE for the integrated one is better as you can see on the page as well as during today's presentation. For the reason why RMSE increase along with the neighbour, I think it is because the more neighbour you have, the more content you have. However, the matrix will become increasingly sparse. And when the postive accuracy of more content is counter-balanced by the negative content effect: so called the sparse matrix effect, then you will have worse RMSE.

Regarding the "Architecture overview of Integration Recommendation System", it is a just flow chart and you can refer to the system overview to get a better understanding for the whole system. I should update this workflow because I have already changed the overview already. I combined both training and testing together to feed the final collaborative filtering in a unified matrix.

Regards Arthur