Context-Aware Personalization: A Systems Engineering Framework

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information (Switzerland) Pub Date : 2023-11-10 DOI:10.3390/info14110608
Olurotimi Oguntola, Steven Simske
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Abstract

This study proposes a framework for a systems engineering-based approach to context-aware personalization, which is applied to e-commerce through the understanding and modeling of user behavior from their interactions with sales channels and media. The framework is practical and built on systems engineering principles. It combines three conceptual components to produce signals that provide content relevant to the users based on their behavior, thus enhancing their experience. These components are the ‘recognition and knowledge’ of the users and their behavior (persona); the awareness of users’ current contexts; and the comprehension of their situation and projection of their future status (intent prediction). The persona generator is implemented by leveraging an unsupervised machine learning algorithm to assign users into cohorts and learn cohort behavior while preserving their privacy in an ethical framework. The component of the users’ current context is fulfilled as a microservice that adopts novel e-commerce data interpretations. The best result of 97.3% accuracy for the intent prediction component was obtained by tokenizing categorical features with a pre-trained BERT (bidirectional encoder representations from transformers) model and passing these, as the contextual embedding input, to an LSTM (long short-term memory) neural network. Paired cohort-directed prescriptive action is generated from learned behavior as a recommended alternative to users’ shopping steps. The practical implementation of this e-commerce personalization framework is demonstrated in this study through the empirical evaluation of experimental results.
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上下文感知个性化:一个系统工程框架
本研究提出了一个基于系统工程的情境感知个性化方法框架,通过理解和建模用户与销售渠道和媒体的互动行为,将其应用于电子商务。该框架是实用的,并且建立在系统工程原理之上。它结合了三个概念组件来产生信号,根据用户的行为提供与用户相关的内容,从而增强他们的体验。这些组件是用户及其行为(角色)的“识别和知识”;对用户当前语境的认知;以及对自己处境的理解和对未来状态的预测(意图预测)。角色生成器是通过利用无监督机器学习算法将用户分配到队列并学习队列行为来实现的,同时在道德框架中保护他们的隐私。用户当前上下文的组件作为采用新颖电子商务数据解释的微服务来实现。通过使用预训练的BERT(来自变压器的双向编码器表示)模型对分类特征进行标记,并将这些特征作为上下文嵌入输入传递给LSTM(长短期记忆)神经网络,获得了97.3%的意图预测组件准确率的最佳结果。配对队列导向的规定行动是从学习行为中生成的,作为用户购物步骤的推荐替代方案。本研究通过对实验结果的实证评价,论证了该电子商务个性化框架的实际实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
期刊最新文献
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