Singular Value Decomposition and Item-Based Collaborative Filtering for Netflix Prize Presentation by Tingda Lu at the Saturday Research meeting 10_23_10 enhanced (with audio added04_30_11) by William perrizo Computer Science North Dakota State University Fargo, ND 58108 USA Agenda Recommendation System Singular Value Decomposition analyzes customer’s purchase history Item-based P-Tree CF algorithm identifies customer’s preference Similarity measurements recommends most likely purchases Experimental results increases customer satisfaction leads to business success SVD amazon.com and Netflix SVD is an important factorization of a rectangular real or matrix, with apps in signal processing and statistics SVD proposed in Netflix by Simon Funk SVD, mathematically, looks nothing like this but engineers, over many years have boiled the technique down into very simple versions (such as this one) for their quick and effective use What about creating and optimizing (with back propagation) a custom matrix for each prediction we have to SVD make? - i.e., in movie-vote.C or user-vote.C. User’s rate movies user preferences about various features of the movie. The call from mpp-user-C to e.g.,with movie-vote.C sends M,U,supM,supU. Features can be anything you wantthe them to beloop (or nothing! randomlyinner constructed!). *** In movie-vote [or user-vote] before entering nested (outer VoterLoop, DimLoop), train T optimal V and matrixes for that # features [pruned supM and supU are << 17K and 500K) InNfact, it is typical to vote startonly with(so a fix numberhigh of meaningless features populated with random values, then back propagate to "improve" those values until some satisfaction level is reached (in terms of the RMSE). This back propagation is identical to that of the back Another option would cluster UT with respect to "max features --> prop be of to Neural Networks. common cosupport" (what ever that might mean - the larger Tingda Lu found 30 features too small and 100 right (200 was too time consuming). the common cosupport is taken to be the smaller the cluster will be. If ccs is required to be too large, many of the cluster may end up being singleton - meaning we are back to where we were before this clustering (no effect). Do the sam e with M. Then train the submatrix pairs (each pair one at a time but note this is entirely parallelizable over a computer cluster. Now each time a pair (u,m) needs a ratings prediction retrieve movies --> the appropriate cluster pair (the VT that contains u and the N that contains m), and apply the ru,i formula below (i=m). users --> features --> Collaborative sim is any simmilarity function. The only req. is /* Movie-based PTree CF*/ Filtering (CF) alg that sim(i.i) >= sim(i,j). In movie-vote.C one SVD training is widely used in could backpropagate train VT and N (see *** on PTree.load_binary(); recommendation Parameters: learning rate and lambda previous slide) anew for each call from mpp-user.C systems to movie-vote.C and thereby allow a large number // Calculate similarity Tune the parameters tothe minimize errorof features (much higher accuracy?) because VT User-based CF algorithm is limited because of its computation complexity Movie-based (Item-based) CF has less scalability concerns while i in I { and N are much smaller than UT and M while j in I { simi,j = sim(PTree[i], Ptree[j]); } } // Get the top K nearest neighbors to item i pt=Ptree.get_items(u); sort(pt.begin(), pt.end(), simi,pt.get_index()); // Prediction of rating on item i by user u sum = 0.0, weight = 0.0; for (j=0; j<K; ++j) { sum += ru,pt[j] * simi,pt[j]; weight += simi,pt[j]; } pred = sum/weight Here Closed Nearest Neighbor methods should improve the result! If the similarity is simple enough to allow the calculation through PTrees, then closed K Nearest Neighbor will be both faster and more accurate. Similarities Adjusted Cosine (correlations) Cosine based SVD item-feature or Tingda Lu similarity? Pearson correlation or combining Pearson and Adj Cosine: * i i j j \ RMSE Neighbor Size Cosine Pearson Adj. Cos SVD IF 1.0742 1.0092 0.9786 0.9865 K=20 1.0629 1.0006 0.9685 0.9900 K=30 1.0602 1.0019 0.9666 0.9972 Two items are not similar if only a few customers purchased or rated both K=40 1.0592 1.0043 0.9960 Co-support is included in item similarity 1.0031 Similarity CorrectionK=10 Prediction Weighted Average Item Effects K=50 1.0589 1.0064 0.9658 Adj Cosine similarity gets much lower RMSE The reason lies in the fact that other algorithms do not exclude the user rating variance Adjusted Cosine algorithm discards the user variance hence gets better prediction accuracy 1.0078 Cosine Pearson Adj. Cos SVD IF Similarity Correction After 1.0589 1.0006 0.9658 0.9865 1.0588 0.9726 1.0637 0.9791 Improve 0.009% 2.798% -10.137% 0.750% Before All algorithms get better RMSE with similarity correction except Adjusted Item Effects Cosine. Cosine Pearson Adj. Cos SVD IF Improvements for all algorithms. 1.0589 1.0006 0.9658 0.9865 Individual’s behavior influenced by others. Before After 0.95750 0.9450 0.9468 0.9381 Improve 9.576% 5.557% 1.967% 4.906% Conclusion Experiments were carried out on Cosine, Pearson, Adjusted Cosine and SVD item-feature algs Support correction and item effects significantly improve the prediction accuracy. Pearson and SVD item-feature algs achieve better results with similarity correction and item effects. Tingda Lu: “Singular Value Decomposition and item-based collaborative filtering for Netflix prize”. As Tingda went through the slides, the group members discussed various issues. Here are some key points of the discussions Saturday (byUMohammad) Participants: Mohammad, Arijit, Using – In the 5th slide,10_23_10 Tingda showed two notes matrices and M. Matrix UT contains the users inArjun, rows and features inSkype the columns. So Tingda and Prakash. there would be 500,000 rows in the matrix (as there are half a million users in the Netflix problem) but number of features is not known (as it is not described in the problem). As Tingda mentioned, you can take as many features as you wish but larger number would give you good result. The value of these features might be randomly filled but they will converge to some values by neural network back propagation. As Tingda found 10 to 30 features are too small, 40 – 60 still not large enough and 100 is good enough. M is the movie matrix where rows represent the features and columns represent the movies. So there are 100 features and 17,000 movies. So it’s a 100x17000 matrix – same thing goes for the features. Arijit suggested that we may go the Netflix’s website to see what the features they use to describe their movies are and we may use those features. In slide no 8, an algorithm is shown for “Item based PTree CF”. The alg 1st calculates similarity between items in the item set I. Here a long discussion took place to choose the similarity function: – Tingda gave 4 similarity fctns; cosine, pearson, adjusted cosine and SVD item feature (shown in slide 9, 10). – Dr. Perrizo's similarity is Sim(i, j) = a positive real number following the property that Sim(i, i) >= Sim(i, j). – Dr. Perrizo made a suggestion of combining the Pearson and Adjusted cosine similarity function as follows: In 2nd part, K nearest nbrs are computed. Dr. P suggested to use Closed KNN. I.e., consider all nbrs same distance as kth Dr. P.:use Sum of Cor (ui, uj), not Nij Then Dr. P.: Use these similarities in use-vote.C and movie-vote.C and get ‘Pruned Training Set Support’ (PTSS) values, which will be used by mpp-user.C to make the final prediction (? ) More features -> more accuracy: In 1, if we include more features that will give us more accuracy in prediction. But we already have too many rows in user matrix (half a million). And we need to train the matrix using back prop (very time consuming). So don’t train matrices before pruning seriously like 10 users so that you can increase number of features. Make code generic (not specific to Netflix problem) so that the code may be used in e.g., satellite imagery – LandSat 5?). 0 rating is not really 0 in Netflix problem should be removed in generic code as 0 may be a valid rating in other problem. Tingda used similarity correction. E.g., he didn’t use 2 items ( or movies) similar if only a few number of users rated both. Tingda's formula: Log(Nij)*Sim(I,j) Dr. Perrizo suggest to use Sum of Cor (ui, uj) instead of Nij