Related papers: Recommender Systems in E-commerce
In recent years, the amount of data available on the internet and the number of users who utilize the Internet have increased at an unparalleled pace. The exponential development in the quantity of digital information accessible and the…
Modern recommender systems operate in uniquely dynamic settings: user interests, item pools, and popularity trends shift continuously, and models must adapt in real time without forgetting past preferences. While existing tutorials on…
Recommender systems are a valuable tool for software engineers. For example, they can provide developers with a ranked list of files likely to contain a bug, or multiple auto-complete suggestions for a given method stub. However, the way…
The use of e-learning systems has a long tradition, where students can study online helped by a system. In this context, the use of recommender systems is relatively new. In our research project, we investigated various ways to create a…
Recommender systems are a subset of information filtering systems designed to predict and suggest items that users may find interesting or relevant based on their preferences, behaviors, or interactions. By analyzing user data such as past…
Recommender systems support decisions in various domains ranging from simple items such as books and movies to more complex items such as financial services, telecommunication equipment, and software systems. In this context,…
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results. Thus, it becomes critical to embrace a trustworthy…
Nowadays, with the increase in the amount of information generated in the webspace, many web service providers try to use recommender systems to personalize their services and make accessing the content convenient. Recommender systems that…
Recommender systems are crucial tools to overcome the information overload brought about by the Internet. Rigorous tests are needed to establish to what extent sophisticated methods can improve the quality of the predictions. Here we…
Recommendation systems are present in multiple contexts as e-commerce, websites, and media streaming services. As scenarios get more complex, techniques and tools have to consider a number of variables. When recommending services/products…
Besides the typical applications of recommender systems in B2C scenarios such as movie or shopping platforms, there is a rising interest in transforming the human-driven advice provided e.g. in consultancy via the use of recommender…
Online e-commerce platforms have been extending in-store shopping, which allows users to keep the canonical online browsing and checkout experience while exploring in-store shopping. However, the growing transition between online and…
Recommender engines have become an integral component in today's e-commerce systems. From recommending books in Amazon to finding friends in social networks such as Facebook, they have become omnipresent. Generally, recommender systems can…
In requirements engineering for recommender systems, software engineers must identify the data that drives the recommendations. This is a labor-intensive task, which is error-prone and expensive. One possible solution to this problem is the…
In this paper, we present our work towards comparing on-line and off-line evaluation metrics in the context of small e-commerce recommender systems. Recommending on small e-commerce enterprises is rather challenging due to the lower volume…
Data and algorithm sharing is an imperative part of data and AI-driven economies. The efficient sharing of data and algorithms relies on the active interplay between users, data providers, and algorithm providers. Although recommender…
In the year 2019, the Recommender Systems Challenge deals with a real-world task from the area of e-tourism for the first time, namely the recommendation of hotels in booking sessions. In this context, this article aims at identifying and…
Recommender systems have been widely applied in different real-life scenarios to help us find useful information. In particular, Reinforcement Learning (RL) based recommender systems have become an emerging research topic in recent years,…
Although Recommender Systems have been comprehensively studied in the past decade both in industry and academia, most of current recommender systems suffer from the following issues: 1) The data sparsity of the user-item matrix seriously…
Algorithms that create recommendations based on observed data have significant commercial value for online retailers and many other industries. Recommender systems have a significant research community, and studying such systems is part of…