Search Results for author: Fabio Crestani

Found 24 papers, 11 papers with code

Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational Search

1 code implementation4 Mar 2024 Ivan Sekulić, Krisztian Balog, Fabio Crestani

One approach expands answers with inline definitions of salient entities, making the answer self-contained.

Conversational Search

Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search

no code implementations21 Jan 2024 Ivan Sekulić, Weronika Łajewska, Krisztian Balog, Fabio Crestani

While the body of research directed towards constructing and generating clarifying questions in mixed-initiative conversational search systems is vast, research aimed at processing and comprehending users' answers to such questions is scarce.

Conversational Search Retrieval

GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval

no code implementations16 Jun 2023 Iain Mackie, Ivan Sekulic, Shubham Chatterjee, Jeffrey Dalton, Fabio Crestani

Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion.

Document Ranking Retrieval

Evaluating Mixed-initiative Conversational Search Systems via User Simulation

1 code implementation17 Apr 2022 Ivan Sekulić, Mohammad Aliannejadi, Fabio Crestani

Clarifying the underlying user information need by asking clarifying questions is an important feature of modern conversational search system.

Conversational Search Text Generation +1

Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation

no code implementations7 Feb 2022 Esteban A. Ríssola, Mohammad Aliannejadi, Fabio Crestani

As for emotional expression, we observe that affected users tend to share emotions more regularly than control individuals on average.

A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

no code implementations20 Jan 2022 Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi, Fabio Crestani

The major contributions of this paper are: (i) providing an extensive survey of context-aware location recommendation (ii) quantifying and analyzing the impact of different contextual information (e. g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, that can incorporate all the major contextual information into a single recommendation model, and (iii) evaluating the considered models using two well-known real-world datasets.

The Impact of User Demographics and Task Types on Cross-App Mobile Search

no code implementations14 Sep 2021 Mohammad Aliannejadi, Fabio Crestani, Theo Huibers, Monica Landoni, Emiliana Murgia, Maria Soledad Pera

Recent developments in the mobile app industry have resulted in various types of mobile apps, each targeting a different need and a specific audience.

Keyword Extraction for Improved Document Retrieval in Conversational Search

no code implementations13 Sep 2021 Oleg Borisov, Mohammad Aliannejadi, Fabio Crestani

Recent research has shown that mixed-initiative conversational search, based on the interaction between users and computers to clarify and improve a query, provides enormous advantages.

Conversational Search Keyword Extraction +2

User Engagement Prediction for Clarification in Search

1 code implementation8 Feb 2021 Ivan Sekulić, Mohammad Aliannejadi, Fabio Crestani

Prompting the user for clarification in a search session can be very beneficial to the system as the user's explicit feedback helps the system improve retrieval massively.

Conversational Search Information Retrieval +1

Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants

1 code implementation9 Jan 2021 Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, W. Bruce Croft

Here we focus on context-aware models to leverage the rich contextual information available to mobile devices.

Management

Longformer for MS MARCO Document Re-ranking Task

1 code implementation20 Sep 2020 Ivan Sekulić, Amir Soleimani, Mohammad Aliannejadi, Fabio Crestani

Two step document ranking, where the initial retrieval is done by a classical information retrieval method, followed by neural re-ranking model, is the new standard.

Document Ranking Information Retrieval +2

A Tool for Conducting User Studies on Mobile Devices

1 code implementation31 Jan 2020 Luca Costa, Mohammad Aliannejadi, Fabio Crestani

With the ever-growing interest in the area of mobile information retrieval and the ongoing fast development of mobile devices and, as a consequence, mobile apps, an active research area lies in studying users' behavior and search queries users submit on mobile devices.

Information Retrieval Retrieval

Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation

1 code implementation24 Jan 2020 Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi, Fabio Crestani

Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem.

Harnessing Evolution of Multi-Turn Conversations for Effective Answer Retrieval

1 code implementation22 Dec 2019 Mohammad Aliannejadi, Manajit Chakraborty, Esteban Andrés Ríssola, Fabio Crestani

With the improvements in speech recognition and voice generation technologies over the last years, a lot of companies have sought to develop conversation understanding systems that run on mobile phones or smart home devices through natural language interfaces.

Retrieval speech-recognition +1

A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation

no code implementations16 Sep 2019 Mohammad Aliannejadi, Dimitrios Rafailidis, Fabio Crestani

In this article, we propose a two-phase CR algorithm that incorporates the geographical influence of POIs and is regularized based on the variance of POIs popularity and users' activities over time.

Collaborative Ranking

LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation

1 code implementation14 Sep 2019 Hossein A. Rahmani, Mohammad Aliannejadi, Sajad Ahmadian, Mitra Baratchi, Mohsen Afsharchi, Fabio Crestani

To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users' and locations' points of view.

Category-Aware Location Embedding for Point-of-Interest Recommendation

no code implementations31 Jul 2019 Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh, Mitra Baratchi, Mohsen Afsharchi, Fabio Crestani

With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation.

Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

2 code implementations15 Jul 2019 Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, W. Bruce Croft

In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems.

Question Selection Retrieval

Suicide Risk Assessment on Social Media: USI-UPF at the CLPsych 2019 Shared Task

no code implementations WS 2019 Esteban R{\'\i}ssola, Diana Ram{\'\i}rez-Cifuentes, Ana Freire, Fabio Crestani

This paper describes the participation of the USI-UPF team at the shared task of the 2019 Computational Linguistics and Clinical Psychology Workshop (CLPsych2019).

Word Embeddings

Venue Suggestion Using Social-Centric Scores

no code implementations22 Mar 2018 Mohammad Aliannejadi, Fabio Crestani

These scores model each user by focusing on the different types of information extracted from venues that they have previously visited.

Comparative Opinion Mining: A Review

no code implementations24 Dec 2017 Kasturi Dewi Varathan, Anastasia Giachanou, Fabio Crestani

Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyse the public opinion on a number of different topics.

Opinion Mining Sentiment Analysis

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