Related papers: Improving Smart Conference Participation through S…
Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the…
In this paper, we present GEVR, the first Group Event Venue Recommendation system that incorporates mobility via individual location traces and context information into a "social-based" group decision model to provide venue recommendations…
Sequential recommendation (SR), which encodes user activity to predict the next action, has emerged as a widely adopted strategy in developing commercial personalized recommendation systems. A critical component of modern SR models is the…
The emergence of smartphones has given mobile computing access to everyday reality. More specifically, the context modeling offers users an effective way to customize search results and even the recommended elements by limiting the data…
Recent advances in Session-based recommender systems have gained attention due to their potential of providing real-time personalized recommendations with high recall, especially when compared to traditional methods like matrix…
This paper focuses on the recommendation of events in the Social Web, and addresses the problem of finding if, and to which extent, certain features, which are peculiar to events, are relevant in predicting the users' interests and should…
User modeling is a very important task for making relevant suggestions of venues to the users. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited…
The Social Internet of Things (SIoT) enables interconnected smart devices to share data and services, opening up opportunities for personalized service recommendations. However, existing research often overlooks crucial aspects that can…
Smart space management can be done in many ways. On one hand, there are conversational assistants such as the Google Assistant or Amazon Alexa that enable users to comfortably interact with smart spaces with only their voice, but these have…
This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life…
Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session. While there are methods that leverage rich context information in sessions for SBR, most of them have the…
Mobile sensing has become a promising paradigm for mobile users to obtain information by task crowdsourcing. However, due to the social preferences of mobile users, the quality of sensing reports may be impacted by the underlying social…
The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper…
Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based…
Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends…
Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in…
We present SmartCourse, an integrated course management and AI-driven advising system for undergraduate students (specifically tailored to the Computer Science (CPS) major). SmartCourse addresses the limitations of traditional advising…
Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user…
Session based recommendation has become one of the research hotpots in the field of recommendation systems due to its highly practical value.Previous deep learning methods mostly focus on the sequential characteristics within the current…
Recommending venues plays a critical rule in satisfying users' needs on location-based social networks. Recent studies have explored the idea of adopting collaborative ranking (CR) for recommendation, combining the idea of learning to rank…