Disagree with most of this. You don't need a massive inventory for recommendations to be useful, and you don't need a massive data set of usage data to be able

Disagree with most of this. You don't need a massive inventory for recommendations to be useful, and you don't need a massive data set of usage data to be able to do them. What really matters is how sparse the user x item matrix is, and I know from experience that you can give ok recommendations even in cases with extremely sparse data. I also don't like the idea it takes mysterious, scary "hefty data science" to be able to do recommendations. - If you're recommending based on one thing (i.e. "people who viewed this also viewed.."), you'll be doing cosine similarity on the vector of viewers (i.e. the columns of the user x item matrix). - If you're recommending based on many things (i.e. "recommended for you"), you'll be doing a matrix factorization of the user x item matrix. Pick SVD or NMF, depending on how sparse the data is. - You probably won't be doing content based recommendations, without doing breakthrough machine learning research. For a lot of content, no-one really knows how to do this.