MRec: Matlab-based Recommender System

MRec is an academic project. It targets for item recommendation from implicit feedback and rating prediction based recommendation. It supports cross validation and holdout evaluation. It includes several important algorithms tailored for implicit feedback, such as WRMF[1], content-aware collaborative filtering for implicit feedback[3], GWMF (graph regularized weighted matrix factorization). Also it includes some state-of-the-art algorithm for location recommendation, such as IRenMF[4], GeoMF[2]. In the future, we will further implement algorithms including Bayesian personalized ranking based matrix factorization, non-negative matrix factorization, Poisson Matrix Factorization.

Usage

It is very simple to use via the portal "item_recommend". The following two parameters are mandatory

• a handle of algorithm, which take a user-item matrix as mandatory input, and other options by name-value pairs.
• a user-item preference matrix.

There are three types of library running schemes, including cross validation and holdout evaluation. (see test/dataset/test_script.m for more examples)

1. The following command specifies cross validation, where 'folds' means the number of folds. And it returns 'metric' which records recall(1:200), precision(1:200), ndcg(1:200), map(1:200), AUC, MPR(Mean Percentile Rank), and 'elapsed' which records training and testing time respectively. There are two rows in recall, precision, ndcg and map, the first row indicate the average value and the second row is the standard deviation of cross validation. For example, recall(1,20) is the averaged recall@20 while recall(2,20) is the standard deviation of cross validation.

[summary, detail, elapsed] = item_recommend(@(mat) iccf(mat, 'K', 50, 'max_iter', 20), data, 'folds', 5); 

which can be rewriten as

[summary, detail, elapsed] = item_recommend(@iccf, data, 'folds', 5, 'K', 50, 'max_iter', 20);

There are five types of matrix split methods, by specified by 'fold_mode', whose first three options

* 'un' (default) user-oriented split, splitting each user data into 'folds' folds.
* 'in' item-oriented split, 
* 'en' entry-oriented split. 
* 'u' splits users into 'folds' folds, which is used for cold-start user evaluation
* 'i' splits items into 'folds' folds, which is used for cold-start item evaluation.

2. The following command specifies holdout evaluation, specified by the ratio of the testing set. Note that it is possible to specify the ratio of the training set by 'train_ratio' (default=1), but it means that in can not be used separately. In other words, it should always be used together with 'test_ratio', since it means it will get the training set from the remaining part by excluding the test set. Besides, it should be accompanied by 'times' option, indicating how many times are need for such holdout evaluation with specified test_ratio.

item_recommend(@(mat) iccf(mat, 'K', 50, 'max_iter', 20), data, 'test_ratio', 0.2, 'times', 5);

There are also five types of matrix split methods, specified by 'split_mode', the same as 'fold_mode'.

3. If you have both training set and test set, using the following commands for training and testing.

item_recommend(@(mat) iccf(mat, 'alpha', 30, 'K', 50, 'max_iter', 20), train, 'test', test);

4. If only dataset is provided, it will return topk recommendation results for each user, where topk is a parameter with defalult value 200.

Besides, it provides some easy-to-use utility function, including hypersearch for searching parameters, readContent to read matrix from tuple-based dataset file/ feature file. You can refer script/locrec/script.m for detailed usage.

Here I would like to emphsis the iccf algorithm[3], since it subsumes weighted regularized matrix factrization, and enables taking any feature from both user side and item side into account. Below I will introduce the usage of this algorithm. A lot of options, including dimension K, maximum number of iteration max_iter, the weight of positive preference alpha, could be specified in this algoritm. In order to specify features from user side and item side, it can specify X as user side feature matrix and Y as item size featur matrix. Below lists some examples

item_recommend(@(mat) iccf(mat, 'K', 50, 'max_iter', 20, 'alpha', 30), data, 'test_ratio', 0.2, 'times', 5);

Note that in this case zeros(size(data,1), 0) is the default value of 'X' and zeros(size(data, 2), 0) is the default value of 'Y', and this is equivelent to WRMF[1].

When item/user features are fed, 'reg_u' and 'reg_i' is used to specify the weight/importance of these features.

item_recommend(@(mat) iccf(mat, 'K', 50, 'max_iter', 20, 'alpha', 30, 'Y', item_featue_matrix, 'reg_i', 100), data, 'test_ratio', 0.2, 'times', 5); 

It is worth mentioning that piccf is a parallel version of iccf. You can specify the same options as iccf.

When using the iccf algorithm for recommendation from implicit feedback datasets, alpha, reg_u and 'reg_i** are three important parameters affecting the recommendation performance. And these parameters may be re-tuned when changing the dimension of latent space.

1. Hu, Y., Koren, Y., & Volinsky, C. Collaborative filtering for implicit feedback datasets. Proceedings of ICDM 2015 (pp. 263-272). IEEE.
2. Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., & Rui, Y. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. Proceedings of SIGKDD 2014 (pp. 831-840). ACM.
3. Lian, D., Ge, Y., Zhang, F., Yuan, N. J., Xie, X., Zhou, T., & Rui, Y. Content-aware collaborative filtering for location recommendation based on human mobility data. Proceedings of ICDM 2015 (pp. 261-270). IEEE.
4. Liu, Y., Wei, W., Sun, A., & Miao, C. Exploiting geographical neighborhood characteristics for location recommendation. Proceedings of CIKM 2014 (pp. 739-748). ACM.