Suppose a user buys n items from my website; I need an algorithm or a method (using Mahout maybe? How?) so that I can recommend k similar items to the user. I don’t have user ratings.
The k recommendations need to be based upon the user’s buying history (his n items).
The items have fields “name”,”author”,”keywords” for example, I need to recommend the most similar items. What happens if I add user ratings along with this? How would I take that into account?
I’ve read the Mahout docs, but it seems to always need some sort of ratings. How will I provide ratings, if say, I have had only a couple of customers so far?
There is no perfect way to build a recommender.
Recommendations without user ratings
Calculate the item-item similarity according to the keywords, name and author. Then you can propose the most similar items not seen yet. As items don’t change often, you can store the similarity-table somewhere.
Recommendations with user ratings
If you don’t want to have user ratings, you could also store the view-history of a user. This results in a “boolean” rating (only having “seen” and “not seen”). With this pseudo-rating, you can generate recommendations with user-similarity. Users having seen similar things are similar.
For some lecture, I strongly recommend you the book Mahout in Action. It contains a lot of information about how to use Mahout.