What's the frequency of the letter "r" in:
What model do they propose?
We do a trick to resolve cold-user problem with global translation vector.
for cold-user, since we don’t have enough information for user specific translation vector. user translation vector dominated by global translation vector.
They apply SBPR (Sequential Bayesian Personalized Ranking) to optimize the formula (1).
They apply SGA (stochastic gradient ascent) to update the parameters.
Bayesian Personalized Ranking (BPR-MF): Taking item recommendation model which takes Matrix Factorization as the underlying predictor without sequential signals.
Factorized Markov Chain (FMC): non personalized model.
Hierarchical Representation Model (HRM): HRM extends FPMC by applying aggregation operations like max pooling to capture non-linear interactions.
FPMC and PRME perform better than FMC and BPR-MF in denser data while perform worse in sparse data. That means non-personalized models perform better in sparse data and personalized models has much power so that it is easily overfitting in sparse data.
TransRec outperforms other methods in nearly all cases.
They utilize content-based to get item feature and add 1 additional embedding E(.).
WNN measures the ‘dis-similarity’ between pairs of items by a weighted Euclidean distance.
LMT learns a single low-rank Mahalanobis transform matrixW to embed all items into relation space.
Monomer extends LMT by learning mixtures of low-rank embeddings to uncover more complex reasons to explain the relationships between items.

user
The given text has ninety-two "r"s.
assistant