Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine

Design and implementation of a WEBGIS-based recommendation system based on context-awareness for tourism planning

Today, tourism is one of the most lucrative industries in the world. Due to the large amount of information that exists about the points of Interest (POI) of a city, the tourist is faced with an overload of information. As a result, a recommending system is needed to recommend suitable tourist places to the tourist in the shortest time. In order to offer a better offer, the interests and contex...

Learning recency based comparative choice towards point-of-interest recommendation

With the prevalence of GPS-enabled smart phones, Location Based Social Network (LBSN) has emerged and become a hot research topic during the past few years. As one of the most important components in LBSN, Points-of-Interests (POIs) has been extensively studied by both academia and industry, yielding POI recommendations to enhance user experience in exploring the city. In conventional methods, ...

compactifications and function spaces on weighted semigruops

chapter one is devoted to a moderate discussion on preliminaries, according to our requirements. chapter two which is based on our work in (24) is devoted introducting weighted semigroups (s, w), and studying some famous function spaces on them, especially the relations between go (s, w) and other function speces are invesigated. in fact this chapter is a complement to (32). one of the main fea...

On-line Weighted Matrix Factorization for TV Program Recommendation

Matrix Factorization (MF) is known as an effective technique in collaborative filtering for recommendation. The MF approaches have often been applied for movie recommender systems which have user rating data. However, they cannot effectively be applicable for TV program recommender systems because (i) explicit rating values are not available; (ii) many TV programs are broadcast under single TV ...

Context-aware factorization methods for implicit feedback based recommendation problems