Tour recommendation and itinerary planning are challenging tasks for tourists, due to their
need to select points of interest (POI) to visit in unfamiliar cities and to select POIs that align …

With the rapidly growing location-based social networks (LBSNs), personalized geo-social
recommendation becomes an important feature for LBSNs. Personalized geo-social …

Collaborative filtering (CF) is an important and popular technology for recommender
systems. However, current CF methods suffer from such problems as data sparsity …

This paper proposes a method that analyzes the location log data of multiple users to
recommend locations to be visited. The method uses our new topic model, called Geo Topic …

Recommender systems are quickly becoming ubiquitous in applications such as e-
commerce, social media channels, and content providers, among others, acting as an …

The rapid development of wireless infrastructure and data acquisition technologies
contributes to the explosive growth of data, especially trajectory data with rich information …

Social recommender systems leverage collaborative filtering (CF) to serve users with
content that is of potential interesting to active users. A wide spectrum of CF schemes has …

Objective Negative symptoms and functional outcome have traditionally been assessed
using clinical rating scales, which rely on retrospective self-reports and have several …

Personalized itinerary recommendation is a complex and time-consuming problem, due to
the need to recommend popular attractions that are aligned to the interest preferences of a …

Geographical influence has been intensively exploited for location recommendations in
location-based social networks (LBSNs) due to the fact that geographical proximity …