Mohammad Aliannejadi

We have employed Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, and Support Vector Machine to improve churn prediction.Using the data of an Iranian mobile company, not only were these techniques experienced and compared to one another, but also we have drawn a parallel between some different prominent data mining software. Expand

Personalized recommendation of Points of Interest (POIs) plays a key role in satisfying users on Location-Based Social Networks (LBSNs). Expand

We present a set of novel scores to measure the similarity between a user and a candidate venue in a new city. Expand

We develop and release a collection of 2,626 open-domain non-factoid questions from a diverse set of categories. Expand

We propose an offline evaluation methodology for the task of asking clarifying questions in open-domain information-seeking conversational systems. Expand

The Contextual Suggestion track at TREC aimed at providing a collection consisting of places as well as user context to enable researchers to examine and compare different approaches, under the same evaluation setting. Expand

We present a set of relevance scores for making personalized suggestions of points of interest, where the users are modeled based on venues’ content as well as social information. Expand

An important task in recommender systems is suggesting relevant venues in a city to a user. Expand

A new POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users' and locations' points of view. Expand

We introduce and study the task of target apps selection, which is defined as identifying the target app(s) for a given query. Expand