user features) user feature matrix 2. FI be the (no. items x no. item features) item feature matrix 3. EU be the (no. user features x latent dimensionality) user feature embedding matrix 4. EI be the (no. item features x latent dimensionality) item feature embedding matrix Then the user-item matrix can be expressed as FU EU (FI EI )T FU and FI are given and we estimate EU and EI .
4 or higher are positives. • In the CrossValidated dataset, answered questions are positives and negatives are randomly sample unanswered questions. Two experiments: • warm-start: random 80%/20% split of all interactions. • cold-start: all interactions for 20% of items are moved to the test set. 12
model using per-user logistic regression models on top of principal components of the item metadata matrix. LSI-UP: a hybrid model that represents user proﬁles as linear combinations of items' content vectors, then applies LSI to the resulting matrix to obtain latent user and item representations. Baselines