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User-generated reviews serve as crucial references in shopper's decision-making process. Moreover, they improve product sales and validate the reputation of the website as a whole. Thus, it becomes important to design reviews ranking…

The explosive growth of information challenges people's capability in finding out items fitting to their own interests. Recommender systems provide an efficient solution by automatically push possibly relevant items to users according to…

We have developed a conversational recommendation system designed to help users navigate through a set of limited options to find the best choice. Unlike many internet scale systems that use a singular set of search terms and return a…

In today's digitalized world, where software systems are becoming increasingly ubiquitous and complex, the quality aspect of explainability is gaining relevance. A major challenge in achieving adequate explanations is the elicitation of…

Recommender systems are central to modern online platforms, but a popular concern is that they may be pulling society in dangerous directions (e.g., towards filter bubbles). However, a challenge with measuring the effects of recommender…

Conversational information access is an emerging research area. Currently, human evaluation is used for end-to-end system evaluation, which is both very time and resource intensive at scale, and thus becomes a bottleneck of progress. As an…

Academic choice is crucial in U.S. undergraduate education, allowing students significant freedom in course selection. However, navigating the complex academic environment is challenging due to limited information, guidance, and an…

Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution…

Recommender systems are present in many web applications to guide our choices. They increase sales and benefit sellers, but whether they benefit customers by providing relevant products is questionable. Here we introduce a model to examine…

In the era of information overload, recommender systems (RSs) have become an indispensable part of online service platforms. Traditional RSs estimate user interests and predict their future behaviors by utilizing correlations in the…

Sequential recommender systems have shown effective suggestions by capturing users' interest drift. There have been two groups of existing sequential models: user- and item-centric models. The user-centric models capture personalized…

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 numeric rating given by a user to reflect his opinion about a…

This paper presents ReasoningRec, a reasoning-based recommendation framework that leverages Large Language Models (LLMs) to bridge the gap between recommendations and human-interpretable explanations. In contrast to conventional…

Many studies in recommender systems (RecSys) adopt a general problem definition, i.e., to recommend preferred items to users based on past interactions. Such abstraction often lacks the domain-specific nuances necessary for practical…

Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on users' preferences or requests. This survey systematically…

Automated decision making is used routinely throughout our everyday life. Recommender systems decide which jobs, movies, or other user profiles might be interesting to us. Spell checkers help us to make good use of language. Fraud detection…

Recommendation systems are widely used in web services, such as social networks and e-commerce platforms, to serve personalized content to the users and, thus, enhance their experience. While personalization assists users in navigating…

Recommender systems have become integral to our digital experiences, from online shopping to streaming platforms. Still, the rationale behind their suggestions often remains opaque to users. While some systems employ a graph-based approach,…

Recommender systems help users to find their appropriate items among large volumes of information. Different types of recommender systems have been proposed. Among these, context-aware recommender systems aim at personalizing as much as…

There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a…