Modeling item--item similarities for personalized recommendations on Yahoo! front page

TL;DR

This paper introduces a novel hierarchical model called UPG that personalizes item recommendations by integrating user covariates and interaction data, employing a graphical lasso for item similarity, and demonstrating superior performance on real and benchmark datasets.

Contribution

The paper presents the UPG model, combining user covariates and interaction data with a graphical lasso approach for scalable, interpretable personalized recommendations, outperforming existing methods.

Findings

UPG significantly outperforms state-of-the-art methods like BIRE.

The model is especially effective with large user sets and small item sets.

UPG offers faster model building and interpretable outputs.

Abstract

We consider the problem of algorithmically recommending items to users on a Yahoo! front page module. Our approach is based on a novel multilevel hierarchical model that we refer to as a User Profile Model with Graphical Lasso (UPG). The UPG provides a personalized recommendation to users by simultaneously incorporating both user covariates and historical user interactions with items in a model based way. In fact, we build a per-item regression model based on a rich set of user covariates and estimate individual user affinity to items by introducing a latent random vector for each user. The vector random effects are assumed to be drawn from a prior with a precision matrix that measures residual partial associations among items. To ensure better estimates of a precision matrix in high-dimensions, the matrix elements are constrained through a Lasso penalty. Our model is fitted through a penalized-quasi likelihood procedure coupled with a scalable EM algorithm. We employ several computational strategies like multi-threading, conjugate gradients and heavily exploit problem structure to scale our computations in the E-step. For the M-step we take recourse to a scalable variant of the Graphical Lasso algorithm for covariance selection. Through extensive experiments on a new data set obtained from Yahoo! front page and a benchmark data set from a movie recommender application, we show that our UPG model significantly improves performance compared to several state-of-the-art methods in the literature, especially those based on a bilinear random effects model (BIRE). In particular, we show that the gains of UPG are significant compared to BIRE when the number of users is large and the number of items to select from is small. For large item sets and relatively small user sets the results of UPG and BIRE are comparable. The UPG leads to faster model building and produces outputs which are interpretable.