A Recommendation refers to the suggestion or advice provided by a network-based system to its users regarding specific actions, choices, or content. These recommendations are often personalized and aim to enhance the user experience, guiding individuals to make informed decisions within the networked environment. Recommendations in networks can span various domains, including content, products, services, connections, and more.

Recommendations in networks play a pivotal role in enhancing user satisfaction, engagement, and the overall utility of the networked platform. Effective recommendation systems contribute to a more personalized and user-friendly experience, fostering a sense of relevance and value for the network users.


Asymmetrically Hierarchical Networks with Attentive Interactions for Interpretable Review-based Recommendation

Recently, recommender systems have been able to emit substantially improved recommendations by leveraging user-provided reviews. Existing methods typically merge all reviews of a given user (item) into a long document, and then process user and item documents in the same manner. In practice, however, these two sets of reviews are notably different: users’ reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item’s reviews pertain only to that single item and are thus topically homogeneous. In this work, we develop a novel neural network model that properly accounts for this important difference by means of asymmetric attentive modules. The user module learns to attend to only those signals that are relevant with respect to the target item, whereas the item module learns to extract the most salient contents with regard to properties of the item. Our multi-hierarchical paradigm accounts for the fact that neither are all reviews equally useful, nor are all sentences within each review equally pertinent. Extensive experimental results on a variety of real datasets demonstrate the effectiveness of our method.