Abstract:
A method and arrangement for processing data when training a data model involving multiple iterations of data records in a dataset (400c, 606) stored in a database (400, 600). Memory space (400d, 608) is allocated in the database for maintaining the data model during the training operation. The data records in the dataset are read (4:3) directly from the database for each iteration, and the data model is trained (4:4) inside the database by repeatedly applying the read data records in the training operation and updating (4:5) the data model. It is also checked (4:6) whether the updated data model has converged according to a predefined convergence condition. The data model is eventually saved (4:7) once the data model has converged.
Abstract:
A method is provided for determining a correlation between a reference user and another user on the basis of two sets of ratings, where each rating is associated with a respective user. In response to a trigger,user ratings associated with the reference user and user ratings associated with the other user are collected, and all co- rated items of these two sets are correlated in the basis of an adjusted cosine correlation function which is weighted by a first and a second weighting function. The correlation is then stored and may be repeated for a plurality of users. The stored correlations may be used e.g. for ranking purposes.