Local Item-Item Models for Top-N Recommendation

Several algorithms for the top-N recommendation problem have been developed. The latent space methods factorize the user-item matrix into lower rank user factor and item factor matrices and the neighborhood-based methods identify similar users or items. The item-based methods, which include item k-NN and Sparse Linear Methods (SLIM) have been shown to outperform the user-based schemes for the top-N recommendation task. However, item-based methods have the drawback of estimating

only a single model for all users.

In this paper they present a top-N recommendation method that extends the SLIM model in order to capture the differences

in the preferences between different user subsets. Their method, which is called GLSLIM (Global and Local SLIM), combines global and local SLIM models in a personalized way and automatically identifies the appropriate user subsets.

GLSLIM computes a global model and has a personalization factor for each user determining the interplay between the global and the local information. GLSLIM updates the assignment of the users to subsets, allowing better local models to be estimated.

Previous works use user and item latent factors, while GLSLIM focuses on item-item models.

A global item-item model may not be sufficient to capture the preferences of a set of users, especially when there are user subsets with diverse and sometimes opposing preferences.

GLSLIM computes top-N recommendations that utilize user-subset specific models and a global model. These models are jointly optimized along with computing the user assignments for them. The estimation of the item-item coefficient matrices, the user assignments and the personalized weight is done with alternating minimization.

To estimate item-item model, they separate the users into subsets with either a clustering algorithm (CLUTO) or randomly. Then

initially set the parameters in order to have equal contribution of the global and the local part and estimate the coefficient matrices. Following SLIM, the item-item coefficient matrices can be calculated per column, which allows for the different columns (of both the global and the local coefficient matrices) to be estimated in parallel. GLSLIM solves an optimization problem using coordinate descent and soft thresholding. After estimating the local models (and the global model), GLSLIM fixes them and proceeds with the second part of the optimization: updating the user subsets. While doing that, GLSLIM also determines the personalized weight.

They employed leave-one-out cross-validation to evaluate the performance of the proposed model. For each user, we randomly selected an item, which we placed in the test set. The rest of the data comprised the training set.