Yeah, since it would appear you're lacking requisite data for recommenders the only other thing I can think of in this case is potentially treating the movie records as documents and clustering them (via whatever might be in the 'description' field).
Have a look here https://cwiki.apache.org/confluence/display/MAHOUT/Quick+tour+of+text+analysis+using+the+Mahout+command+line and see if you can support something like this with your dataset. -----Original Message----- From: Sebastian Schelter [mailto:[email protected]] Sent: Wednesday, February 12, 2014 6:28 AM To: [email protected] Subject: Re: get similar items Hi, Mahout's recommenders are based on analyzing interactions between users and items/movies, e.g. ratings or counts how often the movie was watched. On 02/12/2014 11:34 AM, N! wrote: > Hi all: > Does anyone have any suggestions for the questions below? > > > thanks a lot. > > > ------------------ Original ------------------ > Sender: "N!"<[email protected]>; > Send time: Wednesday, Feb 12, 2014 6:17 PM > To: "user"<[email protected]>; > > Subject: Re: get similar items > > > > Hi Sean: > Thanks for the reply. > Assume I have only one table named 'movie' with 1000+ records, > this table have three columns:'id','movieName','movieDescription'. > Can Mahout calculate the most similar movies for a movie.(based > on only the 'movie' table)? > code like: List mostSimilarMovieList = > recommender.mostSimilar(int movieId). > if not, do you have any suggestions for this scenario? >
