Search Results for author: Mohammad Aliannejadi

Found 42 papers, 24 papers with code

Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts

1 code implementation3 May 2023 Arian Askari, Mohammad Aliannejadi, Evangelos Kanoulas, Suzan Verberne

We introduce a new dataset, ChatGPT-RetrievalQA, and compare the effectiveness of models fine-tuned on LLM-generated and human-generated data.

Re-Ranking Retrieval

On the Impact of Outlier Bias on User Clicks

1 code implementation1 May 2023 Fatemeh Sarvi, Ali Vardasbi, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke

We therefore propose an outlier-aware click model that accounts for both outlier and position bias, called outlier-aware position-based model ( OPBM).


Market-Aware Models for Efficient Cross-Market Recommendation

no code implementations14 Feb 2023 Samarth Bhargav, Mohammad Aliannejadi, Evangelos Kanoulas

We consider the cross-market recommendation (CMR) task, which involves recommendation in a low-resource target market using data from a richer, auxiliary source market.


Experiments on Generalizability of BERTopic on Multi-Domain Short Text

no code implementations16 Dec 2022 Muriël de Groot, Mohammad Aliannejadi, Marcel R. Haas

We further analyze the performance of the HDBSCAN clustering algorithm utilized by BERTopic and find that it classifies a majority of the documents as outliers.


Towards Confidence-aware Calibrated Recommendation

no code implementations22 Aug 2022 Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mohammad Aliannejadi, Nasim Sonboli

Calibration ensures that the distribution of recommended item categories is consistent with the user's historical data.

Recommendation Systems Re-Ranking

Experiments on Generalizability of User-Oriented Fairness in Recommender Systems

1 code implementation17 May 2022 Hossein A. Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, Mohammad Aliannejadi

In this paper, we re-produce a user-oriented fairness study and provide extensive experiments to analyze the dependency of their proposed method on various fairness and recommendation aspects, including the recommendation domain, nature of the base ranking model, and user grouping method.

Fairness Recommendation Systems +1

Understanding User Satisfaction with Task-oriented Dialogue Systems

no code implementations26 Apr 2022 Clemencia Siro, Mohammad Aliannejadi, Maarten de Rijke

What is the influence of user experience on the user satisfaction rating of TDS as opposed to, or in addition to, utility?

Task-Oriented Dialogue Systems

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.

Towards Building Economic Models of Conversational Search

no code implementations21 Jan 2022 Leif Azzopardi, Mohammad Aliannejadi, Evangelos Kanoulas

Various conceptual and descriptive models of conversational search have been proposed in the literature -- while useful, they do not provide insights into how interaction between the agent and user would change in response to the costs and benefits of the different interactions.

Conversational Search Descriptive

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.

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

1 code implementation10 Jan 2022 Kosar Seyedhoseinzadeh, Hossein A. Rahmani, Mohsen Afsharchi, Mohammad Aliannejadi

To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them.

Recommendation Systems

Understanding and Mitigating the Effect of Outliers in Fair Ranking

1 code implementation21 Dec 2021 Fatemeh Sarvi, Maria Heuss, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke

We formalize outlierness in a ranking, show that outliers are present in realistic datasets, and present the results of an eye-tracking study, showing that users scanning order and the exposure of items are influenced by the presence of outliers.

Fairness Outlier Detection

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.

BERT for Target Apps Selection: Analyzing the Diversity and Performance of BERT in Unified Mobile Search

no code implementations13 Sep 2021 Negin Ghasemi, Mohammad Aliannejadi, Djoerd Hiemstra

A unified mobile search framework aims to identify the mobile apps that can satisfy a user's information need and route the user's query to them.

Information Retrieval Retrieval

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 +1

Analysing Mixed Initiatives and Search Strategies during Conversational Search

1 code implementation13 Sep 2021 Mohammad Aliannejadi, Leif Azzopardi, Hamed Zamani, Evangelos Kanoulas, Paul Thomas, Nick Craswel

In this paper, we present a model for conversational search -- from which we instantiate different observed conversational search strategies, where the agent elicits: (i) Feedback-First, or (ii) Feedback-After.

Conversational Search

Cross-Market Product Recommendation

1 code implementation13 Sep 2021 Hamed Bonab, Mohammad Aliannejadi, Ali Vardasbi, Evangelos Kanoulas, James Allan

We introduce and formalize the problem of cross-market product recommendation, i. e., market adaptation.

Domain Adaptation Meta-Learning +1

Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions

1 code implementation EMNLP 2021 Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeffrey Dalton, Mikhail Burtsev

Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response.

Ranking Clarifying Questions Based on Predicted User Engagement

1 code implementation10 Mar 2021 Tom Lotze, Stefan Klut, Mohammad Aliannejadi, Evangelos Kanoulas

This research demonstrates the potential for ranking clarification panes based on lexical information only and can serve as a first neural baseline for future research to improve on.

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

MICROS: Mixed-Initiative ConveRsatiOnal Systems Workshop

no code implementations25 Jan 2021 Ida Mele, Cristina Ioana Muntean, Mohammad Aliannejadi, Nikos Voskarides

The 1st edition of the workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS@ECIR2021) aims at investigating and collecting novel ideas and contributions in the field of conversational systems.

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.


On Estimating the Training Cost of Conversational Recommendation Systems

no code implementations10 Nov 2020 Stefanos Antaris, Dimitrios Rafailidis, Mohammad Aliannejadi

Conversational recommendation systems have recently gain a lot of attention, as users can continuously interact with the system over multiple conversational turns.

Knowledge Distillation Recommendation Systems

ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)

3 code implementations23 Sep 2020 Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, Mikhail Burtsev

The main aim of the conversational systems is to return an appropriate answer in response to the user requests.

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

ANTIQUE: A Non-Factoid Question Answering Benchmark

1 code implementation22 May 2019 Helia Hashemi, Mohammad Aliannejadi, Hamed Zamani, W. Bruce Croft

Despite the importance of the task, the community still feels the significant lack of large-scale non-factoid question answering collections with real questions and comprehensive relevance judgments.

Community Question Answering Passage Retrieval +1

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.

Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data

1 code implementation ALTA 2014 Mohammad Aliannejadi, Masoud Kiaeeha, Shahram Khadivi, Saeed Shiry Ghidary

We experiment graph-based Semi-Supervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language Understanding (SLU) on unaligned data.

Spoken Language Understanding

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