Yes I think the logarithm is a fine choice. The base doesn't matter as the scale of ratings doesn't make a difference.
On Tue, Nov 23, 2010 at 2:07 PM, Sebastian Schelter <[email protected]> wrote: > Hi, > > I'm currently looking into the last.fm dataset (from > http://denoiserthebetter.posterous.com/music-recommendation-datasets) as I'm > planning to write a magazine article or blogpost on howto create a simple > music recommender with Mahout. It should be an easy-to-follow tutorial that > encourages people to download Mahout and play a little with the recommender > stuff. > > The dataset consists of several million > (userID,artist,numberOfPlays)-tuples, and my goal is to find the most > similar artists and recommend new artists to users. I extracted a 20% sample > of the data, ignored the numberOfPlays and used an ItembasedRecommender with > LoglikelihoodSimilarity, did some random tests and got reasonable results. > > Now I wanna go on and include the "strength" of the preference into the > computation. What would be the best way to deal with the numberOfPlays? I > thought about using the log of the numberOfPlays as rating value and > applying PearsonCorrelationSimilarity as measure, would that be a viable way > to approach this problem? > > --sebastian >
