Hi all, I am trying to implement an context-aware recommender in Mahout. As I haven't use the library before I haven't a lot experience. So, I would really appreciate your response!
What I want to do is to implement the two context- aware approaches that have been proposed, pre-filtering and post-filtering. The former filters out the dataset based on the value of contextual factor before the collaborative filtering while the latter rescores the recommendations after the collaborative filtering. I have already read older similar questions regarding the context-aware recommender implementation in mahout and I know that the post-filtering method can be implemented using the IDRescorer. For the pre-filtering approach there is the option to use the CandidateItemsStategy in case of the item-based recommender. On the other hand if we want to implement this approach using the user-bsed recommender no such option is available. In order to implement the pre-filtering using the user-based recommender, I was thinking to filter out the unrelated user,items pairs from the dataset before the creation of the data model. This means that the data model will take as input a subset of the initial dataset. Does this approach sound correct? There are some concerns regarding the evaluation of the recommender. Does it have any impact on this? Thank you in advance! Regards, Efi
