Data Context Adaptation for Accurate Recommendation with Additional Information


Given a sparse rating matrix and an auxiliary matrix of users or items, how can we accurately predict missing ratings considering different data contexts of entities? Many previous studies proved that utilizing the additional information with rating data is helpful to improve the performance. However, existing methods are limited in that 1) they ignore the fact that data contexts of rating and auxiliary matrices are different, 2) they have restricted capability of expressing independence information of users or items, and 3) they assume the relation between a user and an item is linear. We propose DaConA, a neural network based method for recommendation with a rating matrix and an auxiliary matrix. DaConA is designed with the following three main ideas. First, we propose a data context adaptation layer to extract pertinent features for different data contexts. Second, DaConA represents each entity with latent interaction vector and latent independence vector. Unlike previous methods, both of the two vectors are not limited in size. Lastly, while previous matrix factorization based methods predict missing values through the inner-product of latent vectors, DaConA learns a non-linear function of them via a neural network. We show that DaConA is a generalized algorithm including the standard matrix factorization and the collective matrix factorization as special cases. Through comprehensive experiments on real-world datasets, we show that DaConA provides the state-of-the-art accuracy.



The code used in this paper is available here: [].
(It includes the pre-processed datasets.)


Name# Users# Items# Auxiliary rows# Auxiliary columnsAuxiliary data typeDownload
Epinions44,434139,37444,43449,288 User information Link
Ciao-u18,13316,12118,1334,299 User information Link
FilmTrust1,4612,0671,461732 User information Link
MovieLens 1M6,0403,8833,88319 Item information Link
MovieLens 100K9431,6821,68219 Item inforamtion Link
Ciao-i18,13316,12116,12117 Item information Link