My research interests are Big Data, Smart City, Urban Informatics, content-based and collaborative filtering and recommender systems, user-centric information retrieval, adaptive information retrieval, specifically personalised retrieval, implicit feedback-based search systems, affective, and sentiment information retrieval systems.
The basis of this work was to shed a light on the nature of key concepts in information retrieval, i.e. relevance and information need from a Neuropsychological point of view. From a practical point of view, this work can open a new research direction in detecting and prediction both information need and the relevance judgement of searchers by monitoring their brain activities which could lead to the next generation of information systems. The key contribution of this work was the introduction of the first neuropsychological model for the concept of relevance and information need. These three publications have won two best paper awards in ECIR 2013 and ACM SIGIR 2016. Within this work, I learnt how to design and conduct fMRI and EEG based experiments as well as how to process and analyse such big data.
Sample Projects:
As part of my Postdoctoral position, under LiMoSINe project funded under EC Seventh Framework Program scheme, I worked on the topic of multimedia and social media information retrieval. The focus of the project was to develop models and conduct experiments aiming to create a large-scale image (described in Paule, Sun and Moshfeghi, 2018) and Twitter collections (described in Paule, Moshfeghi, Jose and Thakuriah, 2017) as well as to incorporate semantics and contextual information to improve the effectiveness of image (described in Moshfeghi, Agarwal, Piwowarski and Jose, 2009; McParlane, Moshfeghi and Jose, 2014) and twitter retrieval systems (described in McParlane, Moshfeghi and Jose, 2014). Within this work, I learnt the process involved in creating test collections as well as challenges associated with information objects such as multimedia and social media. I have also learnt how to collaborate and supervise PhD students.
Sample Projects:
This work resulted out of a pragmatic need to gather relevance assessment from a crowdsource-based platform. The data gathered from such platforms are known to be noisy due to different worker characteristics. Drawing upon game theory models, we formulated a mechanism design in which risk-averse (poor performing) and risk-inclined (good performing) workers will get automatically identified and eliminated resulting in improving the quality of the data gathered. We have optimised our model via performing an exhaustive simulation (described in Moshfeghi, Rosero and Jose, 2016) and tested it in a crowdsource-based platform, i.e. Amazon Mechanical Turk (described in Moshfeghi, Pinto, Pollick and Jose, 2013). Within this work, I learnt important concepts in game theory such as Nash and Bayesian equilibrium, got familiar with various types of game theatrical models exist both competitive and collaborative ones, and built a mechanism design inspired by Chicken game theory model.
Sample Projects:
The basis of this work was to investigate the possibility of considering affective and physiological signals, gathered from searchers through their search processes, as an indicator of their relevance judgment. Within this work, we considered facial expression as our affective signal. In order to capture physiological signals, we consider multiple sensory channels including heart rate monitoring, skin temperature, and neural activity. We also investigated whether affective and physiological signals can be used as a complementary source of information for behavioural signals (i.e. dwell time) to create a reliable signal for relevance judgement prediction (described in Moshfeghi, Rosero and Jose, 2016). We have also tried to use the relevance judgment inferred from affective signals to improve the recommender systems (Moshfeghi and Jose, 2013; Arapakis, Mosfeghi, Ren and Jose, 2009). Within this work, I learnt, modified and used various supervised machine-learning models such as SVM (linear or with Kernel), Decision Tree, Random Forest, Naïve Bayes, Logistic Regression, etc. (Moshfeghi, Rosero and Jose, 2016; Moshfeghi and Jose, 2013; Arapakis, Moshfeghi, Ren and Jose, 2009). In addition, I learnt how to design and conduct user-based experiments as well as how to process and analyse such data.
Sample Projects:
These papers develop the notion of emotion need, emotion object and emotion relevance, which was the focus of my Doctoral Thesis. In order to investigate the concept of emotion need we have shown that how cognition, emotion, and interaction of searchers differ when they are engaged in search tasks with different intentions (such as seeking, finding, or entertainment) (Arapakis et al., 2009). In order to investigate the concept of emotion object and emotion relevance, we extracted emotion from textual documents using Natural Language Processing techniques (described in Moshfeghi and Jose, 2013) and use this information as a criterion in novel relevance-matching algorithm specifically developed to improve the effectiveness of information retrieval models, particularly in two domains of collaborative filtering (Moshfeghi and Jose, 2013; Moshfeghi, Zuccon and Jose, 2011) and re-ranking search result page (Moshfeghi, Piwowarski and Jose, 2011). We also investigated the complementary aspect of emotion objects with semantic objects (Moshfeghi and Jose, 2013). Within this work, I learnt, modified and used various unsupervised machine-learning models such as KNN (with and without Gaussian Kernels) (Moshfeghi, Zuccon and Jose, 2011; Moshfeghi and Jose, 2011), Latent Dirichlet Allocation and parameter optimization using Boosted Tree (Moshfeghi and Jose, 2013). In addition, I learnt natural language processing and how to build a text-based emotion extraction system.
Sample Projects:
If you are interested in what we are doing, please feel free to find out more on our laboratory website.