M
Mohammad Aliannejadi
Researcher at University of Amsterdam
Publications - 89
Citations - 1545
Mohammad Aliannejadi is an academic researcher from University of Amsterdam. The author has contributed to research in topics: Computer science & Relevance (information retrieval). The author has an hindex of 17, co-authored 80 publications receiving 908 citations. Previous affiliations of Mohammad Aliannejadi include Amirkabir University of Technology & University of Zanjan.
Papers
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Proceedings ArticleDOI
Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
TL;DR: This paper proposed a retrieval framework consisting of three components: question retrieval, question selection, and document retrieval, which takes into account the original query and previous question-answer interactions while selecting the next question.
Journal ArticleDOI
Improved churn prediction in telecommunication industry using data mining techniques
Abbas Keramati,Ruholla Jafari-Marandi,Mohammad Aliannejadi,I. Ahmadian,M. Mozaffari,U. Abbasi +5 more
TL;DR: Data mining classification techniques including Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, and Support Vector Machine are employed to improve churn prediction and a hybrid methodology which made considerable improvements to the value of some of evaluations metrics is proposed.
Proceedings ArticleDOI
Asking Clarifying Questions in Open-Domain Information-Seeking Conversations.
TL;DR: This paper formulate the task of asking clarifying questions in open-domain information-seeking conversational systems, propose an offline evaluation methodology for the task, and collect a dataset, called Qulac, through crowdsourcing, which significantly outperforms competitive baselines.
Journal ArticleDOI
Personalized Context-Aware Point of Interest Recommendation
TL;DR: In this paper, a probabilistic model is proposed to find the mapping between user-annotated tags and locations' taste keywords, and the computed scores are integrated using learning to rank techniques.
Posted Content
ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ).
TL;DR: This document presents a detailed description of the challenge on clarifying questions for dialogue systems (ClariQ) and provides a common evaluation framework to evaluate mixed-initiative conversations.