Things got clearier with your help! Thank you very much
On 9 March 2015 at 01:50, Ted Dunning <ted.dunn...@gmail.com> wrote: > 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. > > > >
