recommendation-engine

I fit a model using scikit-learn NMF model on my training data. Now I perform an inverse transform of new data using result_1 = model.inverse_transform(model.transform(new_data)) Then I compute the inverse transform of my data manually taking the components from the NMF model, using the equation as in Slide 15 here . temp = np.dot(model.components_, model.components_.T) transform = np.dot(np.dot(model.components_.T, np.linalg.pinv(temp)), model.components_) result_2 = np.dot(new_data, transform) I would like to understand why the 2 results do not match. What am I doing wrong while computing

First, I have some use history of user's app. For example: user1, app1, 3(launch times) user2, app2, 2(launch times) user3, app1, 1(launch times) I have basically two demands: Recommend some app for every user. Recommend similar app for every app. So I use ALS(implicit) of MLLib on spark to implement it. At first, I just use the original data to train the model. The result is terrible. I think it may caused by the range of launch times. And the launch time range from 1 to thousands. So I process the original data. I think score can reflect the true situation and more regularization. score = lt

I am trying to build a recommender system which would recommend webpages to the user based on his actions(google search, clicks, he can also explicitly rate webpages). To get an idea the way google news does it, it displays news articles from the web on a particular topic. In technical terms that is clustering, but my aim is similar. It will be content based recommendation based on user's action. So my questions are: How can I possibly trawl the internet to find related web-pages? And what algorithm should I use to extract data from web-page is textual analysis and word frequency the only way

I'm planning to implement a recommendation engine, of which details are given here . 'Python' is my preferred choice along with 'neo4j' Database. Can anyone please point out, how integration of 'neo4j' with any web framework like 'django' can be done?. Is it possible to integrate them just like 'PHP'integrates with 'MySQL'? . Thanks in advance.. Srikar Appalaraju I dont see why not. You can integrate this with Django & serve requests through it... Modeling Categories in Graph Database Neo4J shop categories example Neo4j and django models Django & Neo4j - domain modeling that kicks ass.

I was wondering if there is any open source recommendation engine available? It should suggest something like Amazon and Netflix. I have heard of a framework called Apache Mahout - Taste . I am trying it next week. It would be great if you can share your valuable thoughts. Sean Owen I'm the developer of Mahout / Taste , and hope it will do what you need, but in the interest of balanced coverage, let me also point you at: Duine CoFE Cofi Apache Mahout is the only one I have found for this area (I have been looking recently too). Though Weka may also be an option. I had to work with open source

I'm in the process of designing a website that is built around the concept of recommending various items to users based on their tastes. (i.e. items they've rated, items added to their favorites list, etc.) Some examples of this are Amazon, Movielens, and Netflix. Now, my problem is, I'm not sure where to start in regards to the mathematical part of this system. I'm willing to learn the math that's required, it's just I don't know what type of math is required. I've looked at a few of the publications over at Grouplens.org , specifically " Towards a Scalable kNN CF Algorithm: Exploring

I am puzzled about what the item-based recommendation is, as described in the book " Mahout in Action ". There is the algorithm in the book: for every item i that u has no preference for yet for every item j that u has a preference for compute a similarity s between i and j add u's preference for j, weighted by s, to a running average return the top items, ranked by weighted average How can I calculate the similarity between items? If using the content, isn't it a content-based recommendation? Item-Based Collaborative Filtering The original Item-based recommendation is totally based on user

In mahout you can define a CandidateItemsStrategy for GenericItemBasedRecommender such that specific items e.g. of a certain category are excluded. When using a GenericUserBasedRecommender this is not possible. How can I accomplish this with GenericUserBasedRecommender ? Is the only way to do this using a IDRescorer ? If possible I'd like to avoid using a IDRescorer . Thank you for your help! [Edit] For the item based recommender I do it like this: private final class OnlySpecificlItemsStrategy implements CandidateItemsStrategy { private final JpaDataModel dataModel; public