Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal

The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented

DEVELOPING INNOVATIVE RECOMMENDATION AND PERSONALIZATION ENGINES TO IMPROVE USER EXPERIENCE ON THE TRADING PLATFORM

Abstract: This article explores the development of innovative recommendation and personalization mechanisms to enhance the user experience in a shopping platform. The implementation of effective recommendation engines and personalization techniques in online shopping can significantly improve customer engagement, satisfaction, and conversion rates. The article investigates various algorithms and methods used to develop recommendation engines, including collaborative filtering, content-based filtering, and hybrid approaches. It also delves into personalization techniques such as user profiling, preference analysis, and contextual recommendations. Furthermore, the article discusses the integration of recommendation engines and personalization mechanisms into shopping platforms, considering scalability and real-time recommendations. Evaluation metrics and success indicators are proposed to measure the impact of these mechanisms on user experience and business outcomes. The article concludes by presenting case studies and practical approaches that highlight successful implementations of recommendation and personalization in shopping platforms

Machine learning applications for censored data

The amount of data being gathered has increased tremendously as many aspects of our lives are becoming increasingly digital. Data alone is not useful, because the ultimate goal is to use the data to obtain new insights and create new applications. The largest challenge of computer science has been the largest on the algorithmic front: how can we create machines that help us do useful things with the data? To address this challenge, the field of data science has emerged as the systematic and interdisciplinary study of how knowledge can be extracted from both structed and unstructured data sets. Machine learning is a subfield of data science, where the task of building predictive models from data has been automated by a general learning algorithm and high prediction accuracy is the primary goal. Many practical problems can be formulated as questions and there is often data that describes the problem. The solution therefore seems simple: formulate a data set of inputs and outputs, and then apply machine learning to these examples in order to learn to predict the outputs. However, many practical problems are such that the correct outputs are not available because it takes years to collect them. For example, if one wants to predict the total amount of money spent by different customers, in principle one has to wait until all customers have decided to stop buying to add all of the purchases together to get the answers. We say that the data is ’censored’; the correct answers are only partially available because we cannot wait potentially years to collect a data set of historical inputs and outputs. This thesis presents new applications of machine learning to censored data sets, with the goal of answering the most relevant question in each application. These applications include digital marketing, peer-to-peer lending, unemployment, and game recommendation. Our solution takes into account the censoring in the data set, where previous applications have obtained biased results or used older data sets where censoring is not a problem. The solution is based on a three stage process that combines a mathematical description of the problem with machine learning: 1) deconstruct the problem as pairwise data, 2) apply machine learning to predict the missing pairs, 3) reconstruct the correct answer from these pairs. The abstract solution is similar in all domains, but the specific machine learning model and the pairwise description of the problem depends on the application.Kerätyn datan määrä on kasvanut kun digitalisoituminen on edennyt. Itse data ei kuitenkaan ole arvokasta, vaan tavoitteena on käyttää dataa tiedon hankkimiseen ja uusissa sovelluksissa. Suurin haaste onkin menetelmäkehityksessä: miten voidaan kehittää koneita jotka osaavat käyttää dataa hyödyksi? Monien alojen yhtymäkohtaa onkin kutsuttu Datatieteeksi (Data Science). Sen tavoitteena on ymmärtää, miten tietoa voidaan systemaattisesti saada sekä strukturoiduista että strukturoimattomista datajoukoista. Koneoppiminen voidaan nähdä osana datatiedettä, kun tavoitteena on rakentaa ennustavia malleja automaattisesti datasta ns. yleiseen oppimisalgoritmiin perustuen ja menetelmän fokus on ennustustarkkuudessa. Monet käytännön ongelmat voidaan muotoilla kysymyksinä, jota kuvaamaan on kerätty dataa. Ratkaisu vaikuttaakin koneoppimisen kannalta helpolta: määritellään datajoukko syötteitä ja oikeita vastauksia, ja kun koneoppimista sovelletaan tähän datajoukkoon niin vastaus opitaan ennustamaan. Monissa käytännön ongelmissa oikeaa vastausta ei kuitenkaan ole täysin saatavilla, koska datan kerääminen voi kestää vuosia. Jos esimerkiksi halutaan ennustaa miten paljon rahaa eri asiakkaat kuluttavat elinkaarensa aikana, täytyisi periaatteessa odottaa kunnes yrityksen kaikki asiakkaat lopettavat ostosten tekemisen jotta nämä voidaan laskea yhteen lopullisen vastauksen saamiseksi. Kutsumme tämänkaltaista datajoukkoa ’sensuroiduksi’; oikeat vastaukset on havaittu vain osittain koska esimerkkien kerääminen syötteistä ja oikeista vastauksista voi kestää vuosia. Tämä väitös esittelee koneoppimisen uusia sovelluksia sensuroituihin datajoukkoihin, ja tavoitteena on vastata kaikkein tärkeimpään kysymykseen kussakin sovelluksessa. Sovelluksina ovat mm. digitaalinen markkinointi, vertaislainaus, työttömyys ja pelisuosittelu. Ratkaisu ottaa huomioon sensuroinnin, siinä missä edelliset ratkaisut ovat saaneet vääristyneitä tuloksia tai keskittyneet ratkaisemaan yksinkertaisempaa ongelmaa datajoukoissa, joissa sensurointi ei ole ongelma. Ehdottamamme ratkaisu perustuu kolmeen vaiheeseen jossa yhdistyy ongelman matemaattinen ymmärrys ja koneoppiminen: 1) ongelma dekonstruoidaan parittaisena datana 2) koneoppimista sovelletaan puuttuvien parien ennustamiseen 3) oikea vastaus rekonstruoidaan ennustetuista pareista. Abstraktilla tasolla idea on kaikissa paperissa sama, mutta jokaisessa sovelluksessa hyödynnetään sitä varten suunniteltua koneoppimismenetelmää ja parittaista kuvausta

USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CAMPAIGNS

Organizations allocate a part of their financial resources to optimize their market segmentation strategies, plan marketing campaigns, and improve customer relationships. Throughout this process, they use a vast amount of electronic records generated by online and offline purchases to design effective marketing campaigns and introduce personalized promotions for their customers by employing data analytics. The problem of selecting target customer segments, given various priorities and the budget constraint, can be modeled as a multi-objective optimization problem with flexible goals and different priorities, interdependencies and resources constraints. The main objective of this paper is to demonstrate the use of the goal programming approach to address this challenge

Strategic business models: opportunities for business model innovation in the automotive industry - evaluation of car subscription models and development of recommendations

Digitization and changing customer preferences have an impact on the determinants of sales and ownership in the automotive industry. In this context it is crucial to understand the new, disruptive business models and their potential for market players. The purpose of this report is to evaluate different business models that exist in the automotive industry in Germany and develop recommendations for improving key components of the recently emerged business model of car subscriptions, wherein a customer gains access to a car in return for a flat rate in the medium-term without the transfer of legal ownershi