Related papers: Venue Suggestion Using Social-Centric Scores

Recommender systems are used with the purpose of suggesting contents and resources to the users in a social network. These systems use ranks or tags each user assign to different resources to predict or make suggestions to users. Lately,…

Users are able to access millions of songs through music streaming services like Spotify, Pandora, and Deezer. Access to such large catalogs, created a need for relevant song recommendations. However, user preferences are highly subjective…

To choose restaurants and coffee shops, people are increasingly relying on social-networking sites. In a popular site such as Foursquare or Yelp, a place comes with descriptions and reviews, and with profile pictures of people who frequent…

This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar preferences with their social friends. Which, however, may not…

Collaborative filtering has been largely used to advance modern recommender systems to predict user preference. A key component in collaborative filtering is representation learning, which aims to project users and items into a low…

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…

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…

As the popularity of Location-based Social Networks (LBSNs) increases, designing accurate models for Point-of-Interest (POI) recommendation receives more attention. POI recommendation is often performed by incorporating contextual…

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…

Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on…

Conversational recommender systems (CRS) aim to provide highquality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understanding of the current session, ignoring other rich multi-aspect…

Explaining the output of a complex system, such as a Recommender System (RS), is becoming of utmost importance for both users and companies. In this paper we explore the idea that personalized explanations can be learned as recommendation…

Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation…

Social network based approaches to person recommendations are compared to interest based approaches with the help of an empirical study on a large German social networking platform. We assess and compare the performance of different basic…

Online marketplaces, search engines, and databases employ aggregated social information to rank their content for users. Two ranking heuristics commonly implemented to order the available options are the average review score and item…

We present SceneSuggest: an interactive 3D scene design system providing context-driven suggestions for 3D model retrieval and placement. Using a point-and-click metaphor we specify regions in a scene in which to automatically place and…

Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized…

In this paper we study the venue recommendation problem in order to help researchers to identify a journal or conference to submit a given paper. A common approach to tackle this problem is to build profiles defining the scope of each…

Modeling user preferences (long-term history) and user dynamics (short-term history) is of greatest importance to build efficient sequential recommender systems. The challenge lies in the successful combination of the whole user's history…

Previous studies show that recommendation algorithms based on historical behaviors of users can provide satisfactory recommendation performance. Many of these algorithms pay attention to the interest of users, while ignore the influence of…