tag:blogger.com,1999:blog-590043897961646114.post3372540296550948796..comments2024-02-06T02:06:06.364-08:00Comments on Engaging Market Research: Recommending Recommender Systems When Preferences Are Not Driven By Simple FeaturesUnknownnoreply@blogger.comBlogger2125tag:blogger.com,1999:blog-590043897961646114.post-68758839593525879232015-04-16T11:10:08.832-07:002015-04-16T11:10:08.832-07:00One can think of nonnegative matrix factorization ...One can think of nonnegative matrix factorization (NMF) as principal component analysis or singular value decomposition (SVD) with all the loadings and scores forced to be nonnegative. Nothing ever gets subtracted, so there are no bipolar latent variables for the features and no negative latent variable scores for the respondents. The whole is the sum of the parts, which yields a set of latent variables that does not need rotation to simple structure because the loadings are already sparse. All this depends on separation with different consumer communities focusing on different features (e.g., thin crust seekers do not want all the toppings desired by deep dish lovers). The R package NMF does all the work in a couple of lines of code and helps with the interpretation by including heatmaps. Given all the research that you have already done, you should find my links to NMF posts easy to follow and well worth your time.Joel Cadwellhttps://www.blogger.com/profile/14946447393733294251noreply@blogger.comtag:blogger.com,1999:blog-590043897961646114.post-8862778687790335292015-04-16T10:17:18.168-07:002015-04-16T10:17:18.168-07:00Hi Joel: Your blog is always interesting. Thanks f...Hi Joel: Your blog is always interesting. Thanks for it. I just have a question. I did a little work a few years back with matrix factorization-recommender systems in R. The short of it was took Andre NG's class and then found that estimating recommender systems in R was not trivial on medium-large movie lense data sets. . Results<br />were heavily dependent on which algorithm was used and some algorithms were quite slow and some couldn't arrive at any solution<br />and some were different.<br /><br />So, then I looked I was trying to seeing what the latest and greatest<br />methods were ( literature is enormous ) whether there was a way to implment an SVT<br />( singular value thresholding ) type of approach. I plowed through<br />the literature but, in the end, got stuck working on the SVT implementation. At that time, Hastie and Tibishrani et al submitted a package ( name escapes me at the moment ) that I think sort of does what I just had a dream of doing. so I stopped and left it hanging. <br /><br />But my question is, is there some upside to doing NON NEGATIVE<br />matrix factorization. Does the restriction make sense in the context<br />of recommendation systems. I was never clear on why people<br />tended to use that approach specifically ? and the netflix algorithm literature that went through generally didn't talk about the non-negative approach. Maybe the algorithm is easier ? I have no idea and was just curious about that. Thanks.<br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />mark leedshttps://www.blogger.com/profile/13213841692738932471noreply@blogger.com