Thanks for your reply! Actually, the context in pre-filtering serves as a query for selecting relevant data. An example of a contextual data filter for a movie recommender system would be: if a person wants to see a movie on Saturday, only the Saturday rating data is used to recommend movies. So,what I need for the rating prediction is the data relevent to the specific context.
Regards, Efi On 7 March 2015 at 01:32, Pat Ferrel <p...@occamsmachete.com> wrote: > The new Spark based recommender can easily handle context in many forms. > See the top references section here > http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html > > It does not use the IDRescorer approach at all so perhaps you should > describe what you want to use as context. > > In the demo site for the new stuff (a guide to online video) > https://guide.finderbots.com you’ll see a couple examples of “context”. > For instance you are viewing a video that has several genre tags. You’ll > see at least 3 lists of recommendations: > 1) people who like the video you are looking at also like these other > viedeos—non-personalized recs > 2) people who like this video liked these, from similar genres > 3) personalized recs from all genres based on your “liking” history > > Many other things can be used as context like time of day, location, > mobile or desktop, user profile attributes, etc. The way it does this is > through the search engine, which can take filters and boost certain item > attributes. So I could show only recommendations made in the same year as > the viewed movie or use the year to bias recommendations by boosting the > “release-date” field in the recommender query. The recommender is also > multimodal and so can use many user actions to better the quality of recs. > > Removing some of your data, in what you call pre-filtering may not get you > what you want. Removing data that is actual user behavior can reduce the > quality of recommendations so please give an example. > > On Mar 6, 2015, at 4:45 AM, Efi Koulouri <ekoulou...@gmail.com> wrote: > > 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 > >
