Learning context-dependent choice functions

Pfannschmidt, Karlson; Gupta, Pritha; Haddenhorst, Björn; Hüllermeier, Eyke

doi:10.1016/j.ijar.2021.10.002

Cited by 3 publications

(3 citation statements)

References 54 publications

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“…Loreggia et al [29] proposed a Siamese networks for learning a metric (distance) between set of objects to represent the preferences of two users. Pfannschmidt et al [30] applied neural networks to learn generalized utility functions that are context-dependent. Sifringer et al [31] proposed to divide the utility function into a linear knowledge-driven Logit model and a datadriven non-linear MLP representation learning part for estimating choice models.…”

Section: Deep Representation Learningmentioning

confidence: 99%

Personalized choice prediction with less user information

Chen,

Zhang,

Nguyen

et al. 2024

Ann Math Artif Intell

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While most models of human choice are linear to ease interpretation, it is not clear whether linear models are good models of human decision making. And while prior studies have investigated how task conditions and group characteristics, such as personality or socio-demographic background, influence human decisions, no prior works have investigated how to use less personal information for choice prediction. We propose a deep learning model based on self-attention and cross-attention to model human decision making which takes into account both subject-specific information and task conditions. We show that our model can consistently predict human decisions more accurately than linear models and other baseline models while remaining interpretable. In addition, although a larger amount of subject specific information will generally lead to more accurate choice prediction, collecting more surveys to gather subject background information is a burden to subjects, as well as costly and time-consuming. To address this, we introduce a training scheme that reduces the number of surveys that must be collected in order to achieve more accurate predictions.

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Section: Deep Representation Learningmentioning

confidence: 99%

Personalized choice prediction with less user information

Chen,

Zhang,

Nguyen

et al. 2024

Ann Math Artif Intell

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show abstract

“…While there are numerous LTR methods available in information retrieval, such as those described in [8][9][10], these methods are not tailored to the e-commerce domain. On the contrary, there are other methods available in the e-commerce domain which are designed to meet specific industry requirements [11,12]. That's why we did not include these methods in this research.…”

Section: Ltr Frameworkmentioning

confidence: 99%

A Survey on E-Commerce Learning to Rank

Kabir

Hasan

Mandal³

et al. 2023

Preprint

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In e-commerce, ranking the search results based on users’ preference is the most important task. Commercial e-commerce platforms, such as, Amazon, Alibaba, eBay, Walmart, etc. perform extensive and relentless research to perfect their search result ranking algorithms because the quality of ranking drives a user’s decision to purchase or not to purchase a product, directly affecting the profitability of the e-commerce platform. In such a commercial platforms, for optimizing search result ranking numerous features are considered, which emerge from relevance, personalization, seller’s reputation and paid promotion. To maintain their competitive advantage in the market, the platforms do no publish their core ranking algorithms, so it is difficult to know which of the algorithms or which of the features are the most effective for finding the most optimal search result ranking in e-commerce. No extensive surveys of learning to rank (LTR) in the e-commerce domain as such is not yet published. In this work, we survey the existing e-commerce learning to rank algorithms. Besides, we also compare these algorithms based on query relevance criterion on a large real-life e-commerce dataset and provide a quantitative comparison. To the best of our knowledge this is the first such survey which include an experimental comparison among various learning to rank algorithms.

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“…Preference learning has been a substantial topic in the field of artificial intelligence [50,51]. Learning or eliciting preferences means to acquire preference information in either direct or indirect way, from preference statements, critiques to examples, observation of user's clicking behavior, etc.…”

Section: Related Workmentioning

confidence: 99%

Argumentation with justified preferences

Pyon

2023

Argument & Computation

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It is often necessary and reasonable to justify preferences before reasoning from them. Moreover, justifying a preference ordering is reduced to justifying the criterion that produces the ordering. This paper builds on the well-known ASPIC+ formalism to develop a model that integrates justifying qualitative preferences with reasoning from the justified preferences. We first introduce a notion of preference criterion in order to model the way in which preferences are justified by an argumentation framework. We also adapt the notion of argumentation theory to build a sequence of argumentation frameworks, in which an argumentation framework justifies preferences that are to underlie the next framework. That is, in our formalism, preferences become not only an input of an argumentation framework, but also an output of it. This kind of input-output process can be applied in the further steps of argumentation. We also explore some interesting properties of our formalism.

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Learning context-dependent choice functions

Cited by 3 publications

References 54 publications

Personalized choice prediction with less user information

Personalized choice prediction with less user information

A Survey on E-Commerce Learning to Rank

Argumentation with justified preferences

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