Traditionally, recommender systems operate based on user-behavior and rating data at the
personal and/or aggregate level. In this talk, I will try to go beyond this tradition to discuss …

E-commerce recommender systems seek out matches between customers and items in
order to help customers discover more relevant and satisfying products and to increase the …

User-generated reviews are a plentiful source of user opinions and interests and can play
an important role in a range of artificial intelligence contexts, particularly when it comes to …

Product review sites are widespread on the Internet and are rapidly gaining in popularity
among consumers. This already large volume of user-generated content is dramatically …

Recommender systems play a pivotal role in helping users navigate an overwhelming
selection of products and services. On online platforms, users have the opportunity to share …

In recent years, a variety of review-based recommender systems have been developed, with
the goal of incorporating the valuable information in user-generated textual reviews into the …

Recommender system has been proven to be significantly crucial in many fields and is
widely used by various domains. Most of the conventional recommender systems rely on the …

Sentiment analysis is a trendy domain of Machine Learning which has developed
considerably in the last several years. It helps to determine the sentiment of a user in an …

A recommendation system (RS) is a tool for filtering information that attempts to recommend
interesting items. Globally, RS is based on analyzing users' preferences and their implicit or …

Recommendation systems represent a popular research area with a variety of applications.
Such systems provide personalized services to the user and help address the problem of …