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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Ali Montazeralghaem (Montazer)
About me
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Posts
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Blog Post number 1
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portfolio
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publications
Cross Domain User Engagement Evaluation
Published in The 38th European Conference on Information Retrieval, 2016
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Axiomatic analysis for improving the log-logistic feedback model
Published in The 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016
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Iterative estimation of document relevance score for pseudo-relevance feedback
Published in The 39th European Conference on Information Retrieval, 2017
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Improving Retrieval Performance for Verbose Queries via Axiomatic Analysis of Term Discrimination Heuristic
Published in The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017
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Term Proximity Constraints for Pseudo-Relevance Feedback
Published in The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017
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Theoretical Analysis of Interdependent Constraints in Pseudo-Relevance Feedback
Published in The 41th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018
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Deep Neural Networks for Query Expansion using Word Embeddings
Published in The 41th European Conference on Information Retrieval, 2019
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An Axiomatic Study of Query Terms Order in Ad-hoc Retrieval
Published in The 41th European Conference on Information Retrieval, 2019
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Term Discrimination Value for Cross-Language Information Retrieval
Published in The 2019 ACM SIGIR International Conference on Theory of Information Retrieval, 2019
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Listwise Neural Ranking Models
Published in The 2019 ACM SIGIR international conference on theory of information retrieval, 2019
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An Axiomatic Approach to Corpus-based Cross-language Information Retrieval
Published in Information Retrieval Journal, 2020
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Relevance Ranking Based on Query-Aware Context Analysis
Published in The 42th European Conference on Information Retrieval, 2020
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A Reinforcement Learning Framework for Relevance Feedback
Published in The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020
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Search Result Diversification with Guarantee of Topic Proportionality
Published in The 2020 ACM SIGIR on International Conference on Theory of Information Retrieval, 2020
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Passage Similarity and Diversification in Non-factoid Question Answering
Published in The 2021 ACM SIGIR International Conference on Theory of Information Retrieval, 2021
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Large-scale Interactive Conversational Recommendation System using Actor-Critic Framework
Published in The Fifteenth ACM Conference on Recommender Systems, 2021
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Learning Relevant Questions for Conversational Product Search using Deep Reinforcement Learning
Published in The Fifteenth ACM International Conference on Web Search and Data Mining, 2022
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Extracting Relevant Information from Users Utterances in Conversational Search and Recommendation
Published in The 28th ACM KDD22 Conference on Knowledge Discovery and Data Mining, 2022
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Algorithmic Vibe in Information Retrieval
Published in WWW 23 Proceedings of the ACM Web Conference, 2023
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Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
Published in arxiv, 2023
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talks
Interactive Conversational Search and Recommendation by Deep Reinforcement Learning
Published:
By enabling retrieval and recommender systems to dynamically obtain user preferences through conversations with users, conversational search and recommendation have become increasingly popular in recent years. This process starts with receiving a request from the user and continues with asking clarifying questions or suggesting some possible items or documents by the system. In this way, the system can get valuable feedback from users to accurately determine the users’ needs. This process repeats until the search or recommendation is successful, or the user accepts defeat.
teaching
Introduction to Numerical Computing with Python, Fall 2022
Graduate Course, University of Massachusetts Amherst, 2022
This course is an introduction to computer programming for numerical computing. The course is based on the computer programming language Python and is suitable for students with no programming or numerical computing background who are interested in taking courses in machine learning, natural language processing, or data science. The course will cover fundamental programming, numerical computing, and numerical linear algebra topics, along with the Python libraries that implement the corresponding data structures and algorithms. The course will include hands-on programming assignments and a project.
Search Engines, Fall 2023
Undergraduate Course, University of Massachusetts Amherst, 2023
This course provides an overview of the important issues in information retrieval, and how those issues affect the design and implementation of search engines. The course emphasizes the technology used in Web search engines, and the information retrieval theories and concepts that underlie all search applications.
Introduction to Numerical Computing with Python, Fall 2023
Graduate Course, University of Massachusetts Amherst, 2023
This course is an introduction to computer programming for numerical computing. The course is based on the computer programming language Python and is suitable for students with no programming or numerical computing background who are interested in taking courses in machine learning, natural language processing, or data science. The course will cover fundamental programming, numerical computing, and numerical linear algebra topics, along with the Python libraries that implement the corresponding data structures and algorithms. The course will include hands-on programming assignments and a project.
Search Engines, Spring 2024
Undergraduate Course, University of Massachusetts Amherst, 2024
This course provides an overview of the important issues in information retrieval, and how those issues affect the design and implementation of search engines. The course emphasizes the technology used in Web search engines, and the information retrieval theories and concepts that underlie all search applications.