In Recommender Systems, impressions are a relatively new type of information that records all products previously shown to the users. They are also a complex source of information, combining the effects of the recommender system that generated them, ...

Click-through rate (CTR) Prediction is of great importance in real-world online ads systems. One challenge for the CTR prediction task is to capture the real interest of users from their clicked items, which is inherently influenced by presented ...

Recommender systems are central to online information consumption and user-decision processes, as they help users find relevant information and establish new social relationships. However, recommenders could also (unintendedly) help propagate ...

RecPack is an easy-to-use, flexible and extensible toolkit for top-N recommendation with implicit feedback data. Its goal is to support researchers with the development of their recommendation algorithms, from similarity-based to deep learning algorithms,...

BERT4Rec is an effective model for sequential recommendation based on the Transformer architecture. In the original publication, BERT4Rec claimed superiority over other available sequential recommendation approaches (e.g. SASRec), and it is now ...

Evaluating recommendation systems is a task of utmost importance and a very active research field. While online evaluation is the most reliable evaluation procedure, it may also be too expensive to perform, if not unfeasible. Therefore, researchers and ...

Artificial intelligence (AI) has seen a steady increase in use in the health and medical field, where it is used by lay users and health experts alike. However, these AI systems often lack transparency regarding the inputs and decision making process (...

Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a ...

The constant increase in the amount of data and information available on the Web has made the development of systems that can support users in making relevant decisions increasingly important. Recommender systems (RSs) have emerged as tools to address ...

Recommender Systems are becoming an inherent part of today’s Internet. They can be found anywhere from e-commerce platforms (eBay, Amazon) to music or movie streaming (Spotify, Netflix), social media (Facebook, Instagram, TikTok), travel platforms (...

Creative design is one of the key components of generating engaging content on the web. E-commerce websites need engaging product descriptions, social networks require user posts to have different types of content such as videos, images and hashtags, ...

In this work, we present a scalable and efficient system for exploring the supply landscape in real-time bidding. The system directs exploration based on the predictive uncertainty of models used for click-through rate prediction and works in a high-...

Deep Reinforcement Learning (DRL) uses the best of both Reinforcement Learning and Deep Learning for solving problems which cannot be addressed by them individually. Deep Reinforcement Learning has been used widely for games, robotics etc. Limited work ...

Recommender systems are essential tools to support human decision-making in online information spaces. Many state-of-the-art recommender systems adopt advanced machine learning techniques to model and predict user preferences from behavioral data. While ...

Group Recommender Systems (GRSs), unlike recommendations for individuals, provide suggestions for groups of people. Clearly, many activities are often experienced by a group rather than an individual (visiting a restaurant, traveling, watching a movie, ...

One of the most significant map services in navigation applications is route recommendation. However, most route recommendation systems only recommend trips based on time and distance, impacting quality-of-experience and route selection. This paper ...

Many modern sequential recommender systems use deep neural networks, which can effectively estimate the relevance of items but require a lot of time to train. Slow training increases expenses, hinders product development timescales and prevents the ...

In traditional recommender system literature, diversity is often seen as the opposite of similarity, and typically defined as the distance between identified topics, categories or word models. However, this is not expressive of the social science’s ...

Achieving fairness over different user groups in recommender systems is an important problem. The majority of existing works achieve fairness through constrained optimization that combines the recommendation loss and the fairness constraint. To achieve ...