Related papers: A Business Zone Recommender System Based on Facebo…

In the evolving e-commerce field, recommendation systems crucially shape user experience and engagement. The rise of Consumer-to-Consumer (C2C) recommendation systems, noted for their flexibility and ease of access for customer vendors,…

This paper identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload…

Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user…

Recommender systems have been widely used in various large-scale user-oriented platforms for many years. However, compared to the rapid developments in the AI community, recommendation systems have not achieved a breakthrough in recent…

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…

Recommender systems leverage both content and user interactions to generate recommendations that fit users' preferences. The recent surge of interest in deep learning presents new opportunities for exploiting these two sources of…

Personalized recommendations have become a common feature of modern online services, including most major e-commerce sites, media platforms and social networks. Today, due to their high practical relevance, research in the area of…

In many cases, recommendations are consumed by groups of users rather than individuals. In this paper, we present a system which recommends social events to groups. The system helps groups to organize a joint activity and collectively…

Recommendation systems effectively guide users in locating their desired information within extensive content repositories. Generally, a recommendation model is optimized to enhance accuracy metrics from a user utility standpoint, such as…

In this big data era, it is hard for the current generation to find the right data from the huge amount of data contained within online platforms. In such a situation, there is a need for an information filtering system that might help them…

The majority of existing recommender systems rely on user ratings, which are limited by the lack of user collaboration and the sparsity problem. To address these issues, this study proposes a behavior-based recommender system that leverages…

Recommender systems is one of the most successful AI technologies applied in the internet cooperations. Popular internet products such as TikTok, Amazon, and YouTube have all integrated recommender systems as their core product feature.…

Recommender ecosystems are an emerging subject of research. Such research examines how the characteristics of algorithms, recommendation consumers, and item providers influence system dynamics and long-term outcomes. One architectural…

One of the first things to do while planning a trip is to book a good place to stay. Booking a hotel online can be an overwhelming task with thousands of hotels to choose from, for every destination. Motivated by the importance of these…

In this article, we propose a systematic approach for fire station location planning. We develop machine learning models, based on Random Forest and Extreme Gradient Boosting, for demand prediction and utilize the models further to define a…

Recommender Systems are algorithms that predict a user's preference for an item. Reciprocal Recommenders are a subset of recommender systems, where the items in question are people, and the objective is therefore to predict a bidirectional…

With the increasing popularity of location-based social media applications and devices that automatically tag generated content with locations, large repositories of collaborative geo-referenced data are appearing on-line. Efficiently…

Long-term user engagement (LTE) optimization in sequential recommender systems (SRS) is shown to be suited by reinforcement learning (RL) which finds a policy to maximize long-term rewards. Meanwhile, RL has its shortcomings, particularly…

Session-based recommender systems typically focus on using only the triplet (user_id, timestamp, item_id) to make predictions of users' next actions. In this paper, we aim to utilize side information to help recommender systems catch…

Recommending points of interest (POIs) is a challenging task that requires extracting comprehensive location data from location-based social media platforms. To provide effective location-based recommendations, it's important to analyze…