In the recommender system community, this situation is known as the "cold-start" problem [7, 9], where rare ('cold') entities (e.g., We present Variational Bayesian Network (VBN) - a novel Bayesian unpopular items or new items that are introduced to the catalog) are entity representation learning model that utilizes hierarchical and often poorly represented due to insufficient statistics. In the natural relational side information and is particularly useful for modeling language processing community, where the focus is on learning entities in the "long-tail", where the data is scarce. VBN provides representations for words and phrases, a common mitigation is to better modeling for long-tail entities via two complementary mechanisms: increase the training set size by utilizing increasingly larger corpus First, VBN employs informative hierarchical priors that e.g., BERT [20, 39]. However, it was shown that even when enable information propagation between entities sharing common increasing the amount of co-occurrence data, the existence of rare, ancestors. Additionally, VBN models explicit relations between entities out-of-vocabulary entities persists [26, 50, 52, 53].

Recently, there has been growing interest in the ability of Transformer-based models to produce meaningful embeddings of text with several applications, such as text similarity. Despite significant progress in the field, the explanations for similarity predictions remain challenging, especially in unsupervised settings. In this work, we present an unsupervised technique for explaining paragraph similarities inferred by pre-trained BERT models. By looking at a pair of paragraphs, our technique identifies important words that dictate each paragraph's semantics, matches between the words in both paragraphs, and retrieves the most important pairs that explain the similarity between the two. The method, which has been assessed by extensive human evaluations and demonstrated on datasets comprising long and complex paragraphs, has shown great promise, providing accurate interpretations that correlate better with human perceptions.

We present MetricBERT, a BERT-based model that learns to embed text under a well-defined similarity metric while simultaneously adhering to the ``traditional'' masked-language task. We focus on downstream tasks of learning similarities for recommendations where we show that MetricBERT outperforms state-of-the-art alternatives, sometimes by a substantial margin. We conduct extensive evaluations of our method and its different variants, showing that our training objective is highly beneficial over a traditional contrastive loss, a standard cosine similarity objective, and six other baselines. As an additional contribution, we publish a dataset of video games descriptions along with a test set of similarity annotations crafted by a domain expert.

Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Multi-Persona Collaborative Filtering (AMP-CF) model as a unified solution for both problems. AMP-CF breaks down the user to several latent 'personas' (profiles) that identify and discern the different tastes and inclinations of the user. Then, the revealed personas are used to generate and explain the final recommendation list for the user. AMP-CF models users as an attentive mixture of personas, enabling a dynamic user representation that changes based on the item under consideration. We demonstrate AMP-CF on five collaborative filtering datasets from the domains of movies, music, video games and social networks. As an additional contribution, we propose a novel evaluation scheme for comparing the different items in a recommendation list based on the distance from the underlying distribution of "tastes" in the user's historical items. Experimental results show that AMP-CF is competitive with other state-of-the-art models. Finally, we provide qualitative results to showcase the ability of AMP-CF to explain its recommendations.

An autoencoder is a specific type of a neural network, which is mainlydesigned to encode the input into a compressed and meaningful representation, andthen decode it back such that the reconstructed input is similar as possible to theoriginal one. This chapter surveys the different types of autoencoders that are mainlyused today. It also describes various applications and use-cases of autoencoders.