Efi, Only you can really tell which is best for your efforts. All the rest is our own partially informed opinions.
Pre-filtering can often be accomplished in the search context by creating more than one indicator field and using different combinations of indicators for different tasks. For instance, you could create indicators for last one, two, three, five and seven days. Then when you query the engine, you can pick which indicators to try. That way the same search engine can embody multiple recommendation engines. I would also tend toward search-based approaches for your testing, if only because any deployed system is likely to use a search approach and thus testing that approach in your off-line testing gives you the most realistic results. On Sun, Mar 8, 2015 at 10:21 AM, Efi Koulouri <ekoulou...@gmail.com> wrote: > Thanks for your help! > > Actually, I want to build a recommender for experimental purposes following > the pre-filtering and post-filtering approaches that I described. I have > already two datasets and I want to show the benefits of using a > "context-aware" recommender. So,the recommender is going to work offline. > > I saw that the search engine approach is very interesting but in my case I > think that building the recommender using the java classes is more > appropriate as I need to use both approaches (post filtering,pre > filtering). Am I right ? > > On 8 March 2015 at 16:08, Ted Dunning <ted.dunn...@gmail.com> wrote: > > > The by far easiest way to build a recommender (especially for production) > > is to use the search engine approach (what Pat was recommending). > > > > Post filtering can be done using the search engine far more easily than > > using Java classes. > >
