Either approach could work. In essence they are doing something similar. What works best for your problem will depend on the exact data.
On Sat, Jul 10, 2010 at 12:37 AM, Pradeep Pujari <[email protected]> wrote: > Hi Ted, > > I want to build a prototype for "people who view this item also viewd these > other items" > using Mahout. I am exploring how Mahout could help. I have data like > user_id --> item_id--->no_of_clicks. Looks to me this is not a collaborative > filtering problem. > Because, this is neither finding users having similar taste not similarilty > between items. > I think this is a problem of Co-occurrence discovery and can be solved by > Association Rules Mining > algorithms like FP Growth. Any comment on this is highly appriciated. > > Thanks in advance. > Pradeep > > > On Thu, Jul 8, 2010 at 5:15 PM, Ted Dunning <[email protected]> wrote: > >> The answer to your first question is "yes". >> >> The answer to your second question (please advise) is "heh?" >> >> Can you explain what you are asking in a bit more detail? >> >> On Thu, Jul 8, 2010 at 4:57 PM, Pradeep Pujari <[email protected]> wrote: >> >> > >> > Recommendation Algorithms: Can it be used for a case like, people who >> > viewed >> > this item also viewed these other items? I read the taste recommendation >> > framework which talks about collaborative filtering. Looks to me this >> above >> > use case is not a collaborative filtering subject. We know the click data >> > and math lib can able to help. Please advise. >> > >> > >> >
