Theoretical Framework for Design for Dynamic User Preferences

2021 ◽  
Author(s):  
Mojtaba Arezoomand ◽  
Elliott Rouse ◽  
Jesse Austin-Breneman
Keyword(s):  
Theoretical Framework ◽  
User Preferences ◽  
Dynamic User Preferences

scholarly journals A Survey on Session-based Recommender Systems

2021 ◽  
Vol 54 (7) ◽  
pp. 1-38
Author(s):  
Shoujin Wang ◽  
Longbing Cao ◽  
Yan Wang ◽  
Quan Z. Sheng ◽  
Mehmet A. Orgun ◽  
...  
Keyword(s):  
Recommender Systems ◽  
User Preferences ◽  
Session Length ◽  
New Paradigm ◽  
Problem Statement ◽  
Short Term ◽  
Comprehensive Review ◽  
New Research ◽  
Dynamic User Preferences

Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs that usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. It is also unclear to what extent SBRS challenges have been addressed and what the overall research landscape of SBRSs is. This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users’ clicks on items), and their properties (e.g., session length). We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. Finally, we discuss new research opportunities in this exciting and vibrant area.

Theoretical Framework for Design for Dynamic User Preferences

2020 ◽  
Author(s):  
Mojtaba Arezoomand ◽  
Elliott Rouse ◽  
Jesse Austin-Breneman
Keyword(s):  
New Product Development ◽  
User Satisfaction ◽  
Variable Stiffness ◽  
User Preferences ◽  
Illustrative Case ◽  
Design Strategies ◽  
Initial Information ◽  
Upper Confidence Bound ◽  
Process Length ◽  
Dynamic User Preferences

Abstract A key assumption of new product development is that user requirements and related preferences do not vary on time scales of the process length. However, prior work has identified cases in which user preferences for product attributes can vary with time. This study proposes a method, Design for Dynamic User Preferences, which adapts reinforcement learning (RL) algorithms for designing physical systems whose functionality changes with user feedback. An illustrative case comprised of the design of a variable stiffness prosthetic ankle is presented to evaluate the potential usefulness of the framework. Lifetime user satisfaction for static and dynamic design strategies are compared over simulated user preferences under a number of conditions. Results suggest RL-based strategies outperform static strategies for cases with dynamic user preferences despite significantly less initial information. Within RL methods, upper-confidence bound policies led to higher user satisfaction on average. This study suggests that further investigation into RL-based design strategies is warranted for situations with possibly dynamic preferences.

scholarly journals About time

2019 ◽  
Vol 21 (2) ◽  
pp. 236-254
Author(s):  
Ville Salonen ◽  
Heikki Karjaluoto
Keyword(s):  
Theoretical Basis ◽  
Design Methodology ◽  
Temporal Dynamics ◽  
User Preferences ◽  
Optimal Timing ◽  
User Models ◽  
Content Type ◽  
Practical Implications ◽  
User Motivation ◽  
Dynamic User Preferences

Purpose The purpose of this paper seeks to develop a motivation-based complementary framework for temporally dynamic user preferences to facilitate optimal timing in web personalisation. It also aims to highlight the benefits of considering user motivation when addressing issues in temporal dynamics. Design/methodology/approach Through theory, a complementary framework and propositions for motivation-based temporal dynamics for further testing are created. The framework is validated by feeding back findings, whereas some of the propositions are validated through an experiment. Findings The suggested framework distinguishes two ways (identifying/learning and shifting) of using a motive-based approach to temporal dynamics in web personalisation. The suggested outcomes include enhanced timing in matching current preferences and improved conversion. Validation measures predominantly support both the framework and the tested propositions. The theoretical basis for the approach paves a path towards refined psychological user models; however, currently on a complementary level. Research limitations/implications While the framework is validated through feeding back findings, and some of the propositions are validated through basic experimentation, further empirical testing is required. Practical implications A generalised approach for complementing personalisation procedures with motivation-based temporal dynamics is offered, with implications for both user modelling and preference matching. Originality/value This paper offers novel insights to web personalisation by considering the in-depth effects of user motivation.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Adaptive and Maladaptive Neural Plasticity Due to Facial Nerve Palsy

2016 ◽  
Vol 224 (2) ◽  
pp. 102-111 ◽  
Author(s):  
Carsten M. Klingner ◽  
Stefan Brodoehl ◽  
Gerd F. Volk ◽  
Orlando Guntinas-Lichius ◽  
Otto W. Witte
Keyword(s):  
Facial Nerve ◽  
Neural Plasticity ◽  
Cortical Plasticity ◽  
Brain Plasticity ◽  
Motor Nerve ◽  
Predictive Coding ◽  
Theoretical Framework ◽  
Cortical Reorganization ◽  
Nerve Lesion ◽  
Peripheral Facial Palsy

Abstract. This paper reviews adaptive and maladaptive mechanisms of cortical plasticity in patients suffering from peripheral facial palsy. As the peripheral facial nerve is a pure motor nerve, a facial nerve lesion is causing an exclusive deefferentation without deafferentation. We focus on the question of how the investigation of pure deefferentation adds to our current understanding of brain plasticity which derives from studies on learning and studies on brain lesions. The importance of efference and afference as drivers for cortical plasticity is discussed in addition to the crossmodal influence of different competitive sensory inputs. We make the attempt to integrate the experimental findings of the effects of pure deefferentation within the theoretical framework of cortical responses and predictive coding. We show that the available experimental data can be explained within this theoretical framework which also clarifies the necessity for maladaptive plasticity. Finally, we propose rehabilitation approaches for directing cortical reorganization in the appropriate direction and highlight some challenging questions that are yet unexplored in the field.

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