As per your question, we have not built anything yet so, we are dealing with that problem: How to let the tweets drive the recommendation of the news to be viewed.
The original idea was to find item-item similarity between the user tweets and the news in order to deal with the cold-start problem and infer some initial preference of the users and the news based on that item-item similarity. This is where my original idea of using RowSimilarityJob to compute the matrix of similarities came into place. Later, as the user accesses different news those preferences will we tuned as in a regular item-based recommender. Since the system has not been built yet, our first goal is to design the architecture of the system first and how it should respond after new tweets are produced, even if the performance is not the best in this first version. Then, we will focus on the particular problem of using tweets to recommend news, for which the links you posted will be extremely helpful. I am new to Mahout. I have just finished reading 'Mahout in Action' and that is why I tried to use only Mahout for the implementation, but the approach you suggest with Solr seems more reasonable to deal with the problem of having the system responding and adapting fast when new tweets are produced. Thanks again.
