Related papers: CF4J: Collaborative Filtering for Java

The use of relevant metrics of software systems could improve various software engineering tasks, but identifying relationships among metrics is not simple and can be very time consuming. Recommender systems can help with this…

Rs4rs is a web application designed to perform semantic search on recent papers from top conferences and journals related to Recommender Systems. Current scholarly search engine tools like Google Scholar, Semantic Scholar, and ResearchGate…

Collaborative Filtering (CF) has become the standard approach to solve recommendation systems (RS) problems. Collaborative Filtering algorithms try to make predictions about interests of a user by collecting the personal interests from…

Group Recommender Systems (GRSs) have been studied and developed for more than twenty years. However, their application and usage has not grown. They can even be labeled as failures, if compared to the very successful and common recommender…

Project ATHENA aims to develop an application to address information overload, primarily focused on Recommendation Systems (RSs) with the personalization and user experience design of a modern system. Two machine learning (ML) algorithms…

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation.…

Recommendation system is such a platform that helps people to easily find out the things they need within a few seconds. It is implemented based on the preferences of similar users or items. In this digital era, the internet has provided us…

Owing to the impressive general intelligence of large language models (LLMs), there has been a growing trend to integrate them into recommender systems to gain a more profound insight into human interests and intentions. Existing LLMs-based…

Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary…

Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited…

In Conversational Recommendation Systems (CRS), a user can provide feedback on recommended items at each interaction turn, leading the CRS towards more desirable recommendations. Currently, different types of CRS offer various possibilities…

Recommender systems (RSs) have emerged as very useful tools to help customers with their decision-making process, find items of their interest, and alleviate the information overload problem. There are two different lines of approaches in…

Conversational Recommender System (CRS), which aims to recommend high-quality items to users through interactive conversations, has gained great research interest recently. A CRS is usually composed of a recommendation module and a…

Collaborative Filtering (CF) is one of the most commonly used recommendation methods. CF consists in predicting whether, or how much, a user will like (or dislike) an item by leveraging the knowledge of the user's preferences as well as…

In recent years, text-aware collaborative filtering methods have been proposed to address essential challenges in recommendations such as data sparsity, cold start problem, and long-tail distribution. However, many of these text-oriented…

Using 286 research papers collected from Web of Science, ScienceDirect, SpringerLink, arXiv, and Google Scholar databases, a systematic review methodology was adopted to review and summarize the current challenges and potential future…

A recommender system (RS) aims to provide users with personalized item recommendations, enhancing their overall experience. Traditional RSs collect and process all user data on a central server. However, this centralized approach raises…

The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). Thus, two main issues have to be considered: assist users in finding information and reduce search and…

Recommender systems (RS), which have been an essential part in a wide range of applications, can be formulated as a matrix completion (MC) problem. To boost the performance of MC, matrix completion with side information, called inductive…

Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predominantly used raises serious privacy concerns. The federated…