Sequential consumer choice as multi-cued retrieval

Whether adding songs to a playlist or groceries during an online shop, how do we decide what to choose next? We develop a model that predicts such open-ended, sequential choices using a process of cued retrieval from long-term memory. Using the past choice to cue subsequent retrievals, this model predicts the sequential purchases and response times of nearly 5 million grocery purchases made by more than 100,000 online shoppers. Products can be associated in different ways, such as by their episodic association or semantic overlap, and we find that consumers query multiple forms of associative knowledge when retrieving options. Attending to certain knowledge sources, as estimated by our model, predicts important retrieval errors, such as the propensity to forget or add unwanted products. Our results demonstrate how basic memory retrieval mechanisms shape choices in real-world, goal-directed tasks

Memory-based preferential choice in large option spaces

Whether adding songs to a playlist or groceries to a shopping basket, everyday decisions often require us to choose between an innumerable set of options. Laboratory studies of preferential choice have made considerable progress in describing how people navigate fixed sets of options. Yet, questions remain about how well this generalises to more complex, everyday choices. In this thesis, I ask how people navigate large option spaces, focusing particularly on how long-term memory supports decisions. In the first project, I explore how large option spaces are structured in the mind. A topic model trained on the purchasing patterns of consumers uncovered an intuitive set of themes that centred primarily around goals (e.g., tomatoes go well in a salad), suggesting that representations are geared to support action. In the second project, I explore how such representations are queried during memory-based decisions, where options must be retrieved from memory. Using a large dataset of over 100,000 online grocery shops, results revealed that consumers query multiple systems of associative memory when determining what choose next. Attending to certain knowledge sources, as estimated by a cognitive model, predicted important retrieval errors, such as the propensity to forget or add unwanted products. In the final project, I ask how preferences could be learned and represented in large option spaces, where most options are untried. A cognitive model of sequential decision making is proposed, which learns preferences over choice attributes, allowing for the generalisation of preferences to unseen options, by virtue of their similarity to previous choices. This model explains reduced exploration patterns behaviour observed in the supermarket and preferential choices in more controlled laboratory settings. Overall, this suggests that consumers depend on associative systems in long-term memory when navigating large spaces of options, enabling inferences about the conceptual properties and subjective value of novel options

Implications of Computational Cognitive Models for Information Retrieval

This dissertation explores the implications of computational cognitive modeling for information retrieval. The parallel between information retrieval and human memory is that the goal of an information retrieval system is to find the set of documents most relevant to the query whereas the goal for the human memory system is to access the relevance of items stored in memory given a memory probe (Steyvers & Griffiths, 2010). The two major topics of this dissertation are desirability and information scent. Desirability is the context independent probability of an item receiving attention (Recker & Pitkow, 1996). Desirability has been widely utilized in numerous experiments to model the probability that a given memory item would be retrieved (Anderson, 2007). Information scent is a context dependent measure defined as the utility of an information item (Pirolli & Card, 1996b). Information scent has been widely utilized to predict the memory item that would be retrieved given a probe (Anderson, 2007) and to predict the browsing behavior of humans (Pirolli & Card, 1996b). In this dissertation, I proposed the theory that desirability observed in human memory is caused by preferential attachment in networks. Additionally, I showed that documents accessed in large repositories mirror the observed statistical properties in human memory and that these properties can be used to improve document ranking. Finally, I showed that the combination of information scent and desirability improves document ranking over existing well-established approaches

Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces

Knowledge graph embedding (KGE) is a increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning and knowledge completion. In this paper, we provide a systematic review of existing KGE techniques based on representation spaces. Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) Algebraic perspective, (2) Geometric perspective, and (3) Analytical perspective. We introduce the rigorous definitions of fundamental mathematical spaces before diving into KGE models and their mathematical properties. We further discuss different KGE methods over the three categories, as well as summarise how spatial advantages work over different embedding needs. By collating the experimental results from downstream tasks, we also explore the advantages of mathematical space in different scenarios and the reasons behind them. We further state some promising research directions from a representation space perspective, with which we hope to inspire researchers to design their KGE models as well as their related applications with more consideration of their mathematical space properties.Comment: 32 pages, 6 figure

Perceptually Driven Simulation

This dissertation describes, implements and analyzes a comprehensive system for perceptually-driven virtual reality simulation, based on algorithms which dynamically adjust level of detail (LOD) for entity simulation in order to maximize simulation realism as perceived by the viewer. First we review related work in simulation LOD, and describe the weaknesses of the analogy that has traditionally been drawn between simulation LOD and graphical LOD. We describe the process of perceptual criticality modeling for quantitatively estimating the relative importance of different entities in maintaining perceived realism and predicting the consequences of LOD transitions on perceived realism. We present heuristic cognitive models of human perception, memory, and attention to perform this modeling. We then propose the LOD Trader , a framework for perceptually driven LOD selection and an online approximation algorithm for efficiently identifying useful LOD transitions. We then describe alibi generation , a method of retroactively elaborating a human agent\u27s behavior to maintain its realism under prolonged scrutiny from the viewer, and discuss its integration into a heterogeneous perceptually driven simulation. We then present the Marketplace simulation system and describe how perceptually driven simulation techniques were used to maximize perceived realism, and evaluate their success in doing so. Finally, we summarize the dissertation work performed and its expected contributions to real-time modeling and simulation environments

Analog Photonics Computing for Information Processing, Inference and Optimisation

This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

A Knowledge Multidimensional Representation Model for Automatic Text Analysis and Generation: Applications for Cultural Heritage

Knowledge is information that has been contextualized in a certain domain, where it can be used and applied. Natural Language provides a most direct way to transfer knowledge at different levels of conceptual density. The opportunity provided by the evolution of the technologies of Natural Language Processing is thus of making more fluid and universal the process of knowledge transfer. Indeed, unfolding domain knowledge is one way to bring to larger audiences contents that would be otherwise restricted to specialists. This has been done so far in a totally manual way through the skills of divulgators and popular science writers. Technology provides now a way to make this transfer both less expensive and more widespread. Extracting knowledge and then generating from it suitably communicable text in natural language are the two related subtasks that need be fulfilled in order to attain the general goal. To this aim, two fields from information technology have achieved the needed maturity and can therefore be effectively combined. In fact, on the one hand Information Extraction and Retrieval (IER) can extract knowledge from texts and map it into a neutral, abstract form, hence liberating it from the stylistic constraints into which it was originated. From there, Natural Language Generation can take charge, by regenerating automatically, or semi-automatically, the extracted knowledge into texts targeting new communities. This doctoral thesis provides a contribution to making substantial this combination through the definition and implementation of a novel multidimensional model for the representation of conceptual knowledge and of a workflow that can produce strongly customized textual descriptions. By exploiting techniques for the generation of paraphrases and by profiling target users, applications and domains, a target-driven approach is proposed to automatically generate multiple texts from the same information core. An extended case study is described to demonstrate the effectiveness of the proposed model and approach in the Cultural Heritage application domain, so as to compare and position this contribution within the current state of the art and to outline future directions