PEMODELAN SISTEM REKOMENDASI CERDAS MENGGUNAKAN HYBRID DEEP LEARNING

Trend perkembangan teknologi saat ini adalah mengarah ke sistem cerdas. Namun saat ini belum ada yang menggabungkan dua metode deep learning pada algoritma rekomendasi, sehingga penting untuk melakukan pemodelan sistem rekomendasi cerdas menggunakan hybrid deep learning. Penelitian ini bertujuan untuk mendapatkan model hybrid deep learning yang optimal pada sistem rekomendasi cerdas. Penelitian ini menggunakan pendekatan penelitian eksperimen. Teknik pengumpulan data yang digunakan meliputi observasi, penelusuran online dan pencatatan dataset. Tahapan penelitian terdiri dari: (a) literature review, (b) observasi dan pencarian online, (c) modeling (d) prototyping dan (e) testing. Model yang dihasilkan merupakan model hybrid deep learning yang terdiri dari dua layer yaitu layer Self Organizing Map (SOM) dan layer Recurrent Neural Network (RNN). Penelitian ini menggunakan bahasa pemrograman Python pada tahap pembuatan prototipe. Beberapa modul library dalam python yang digunakan antara lain numpy, pandas, tensorflow, hard, torch, sklearn. Program diuji dengan dataset dari kaggle.com. Hasil pengujian berhasil meningkatkan kinerja dengan meningkatkan akurasi hingga 100%. Dapat disimpulkan bahwa model SOM-RNN dapat meningkatkan kinerja sistem rekomendasi cerdas

IMPUTING OR SMOOTHING? MODELLING THE MISSING ONLINE CUSTOMER JOURNEY TRANSITIONS FOR PURCHASE PREDICTION

Online customer journeys are at the core of e-commerce systems and it is therefore important to model and understand this online customer behaviour. Clickstream data from online journeys can be modelled using Markov Chains. This study investigates two different approaches to handle missing transition probabilities in constructing Markov Chain models for purchase prediction. Imputing the transition probabilities by using Chapman-Kolmogorov (CK) equation addresses this issue and achieves high prediction accuracy by approximating them with one step ahead probability. However, it comes with the problem of a high computational burden and some probabilities remaining zero after imputation. An alternative approach is to smooth the transition probabilities using Bayesian techniques. This ensures non-zero probabilities but this approach has been criticized for not being as accurate as the CK method, though this has not been fully evaluated in the literature using realistic, commercial data. We compare the accuracy of the purchase prediction of the CK and Bayesian methods, and evaluate them based on commercial web server data from a major European airline

E-ticaret alanı için sipariş iptallerini tahmin etme: Perakendecilik deneyimine dayalı önerilen bir model

E-Commerce technologies enable contact between businesses and their suppliers for the aim of exchanging information such as purchase orders, invoices, and payments thank to the rapid development in information technologies. E-Commerce has become a particularly important concept and has revolutionized the retail space. Understanding customer behavior patterns is key to gaining competitive advantage and achieving business goals. Predicting the probability of order cancellations has become a very urgent need as it causes loss of revenue for the retailer. When dealing with day-to-day operations such as order processing, tracking and order cancellations, finding enough time to grow the business is difficult. Cancellations are an important aspect of retail industry revenue management. In fact, little is known about the factors that cause customers to cancel or how to avoid them. The aim of this study is to propose a model that predicts the tendency to cancel an order and the parameters that affect the cancellation of the order. This solution can identify key factors that cause orders to be canceled by analyzing historical transaction data. A custom modeling application has been created that helps automate the process of tracking order cancellations in real time and predict the probability of an order being cancelled. For this purpose, machine learning techniques (ML) such as Artificial Neural Network, Support Vector Machine, Linear and Logistic Regression, XGBoost, Random Forest are applied to provide a tool for predicting order cancellations. The Random Forest algorithm achieves the best performance with 86% accuracy and 88% F1-Score compared to the other algorithm. This work will help firms manage their inventories well and strengthen their actions regarding customer behavior.E-Ticaret teknolojileri, bilgi teknolojilerindeki hızlı gelişme sayesinde, işletmelerin satın alma siparişleri, faturalar, ödemeler gibi bilgi alışverişi amacıyla tedarikçileri ile iletişim kurmasını sağlamaktadır. E-Ticaret özellikle önemli bir kavram haline gelmiştir ve perakende alanında devrim yaratmıştır. Müşteri davranış kalıplarını anlamak, rekabet avantajı elde etmenin ve iş hedeflerine ulaşmanın anahtarıdır. Perakendeci için gelir kaybına neden olduğu için sipariş iptallerinin olasılığını tahmin etmek çok acil bir ihtiyaç haline gelmiştir. Sipariş işleme, takip ve sipariş iptalleri gibi günlük işlemlerle uğraşırken, işi büyütmek için yeterli zaman bulmak zordur. İptaller, perakende sektörü gelir yönetiminin önemli bir yönüdür. Aslında, müşterilerin iptal etmesine neden olan faktörler veya bunlardan nasıl kaçınılacağı hakkında çok az şey bilinmektedir. Bu çalışmanın amacı, bir siparişi iptal etme eğilimini ve siparişin iptalini etkileyen parametreleri tahmin eden bir model önermektir. Bu çözüm, geçmiş işlem verilerini analiz ederek siparişlerin iptal edilmesine neden olan temel faktörleri belirleyebilir. Sipariş iptallerini gerçek zamanlı olarak izleme sürecini otomatikleştirmeye ve bir siparişin iptal edilme olasılığını tahmin etmeye yardımcı olan özel bir modelleme uygulaması oluşturulmuştur. Bu amaçla Yapay Sinir Ağı, Destek Vektör Makinesi, Doğrusal ve Lojistik Regresyon, XGBoost, Rastgele Orman gibi makine öğrenme teknikleri uygulanarak sipariş iptallerini tahmin etme aracı sağlanmıştır. Rastgele Ormanalgoritması diğer algoritmaya göre %86 doğruluk oranı ve %88 F1-Score ile en iyi performansı elde etmektedir. Bu çalışma, firmaların envanterlerini iyi yönetmelerine ve müşteri davranışlarıyla ilgili eylemlerini güçlendirmelerine yardımcı olacaktır

Log-Based Session Profiling and Online Behavioral Prediction in E-Commerce Websites

Improvements to customer experience give companies a competitive advantage, as understanding customers' behaviors allows e-commerce companies to enhance their marketing strategies by means of recommendation techniques and the customization of products and services. This is not a simple task, and it becomes more difficult when working with anonymous sessions since no historical information of the user can be applied. In this article, analysis and clustering of the clickstreams of past anonymous sessions are used to synthesize a prediction model based on a neural network. The model allows for prediction of a user's profile after a few clicks of an online anonymous session. This information can be used by the e-commerce's decision system to generate online recommendations and better adapt the offered services to the customer's profile

Asiakasymmärryksen hallinta markkinointiautomaatiolla

Tutkielmassa tutkitaan eri toimialojen yritysten markkinointiautomaation käyttöä asiantuntijahaastattelujen avulla. Tutkielman tavoitteena on selvittää, miten yrityksen lähestyvät markkinointiautomaatiota löytääkseen onnistumisia ja puutoskohtia nykyisessä toiminnassa. Tutkimusaineistossa havaittiin asiakasymmärryksen konsepti, mikä on välttämätön liiketoiminnan harjoittamiselle. Asiakasymmärryksen konseptin tiedostamisella tutkimuksessa laadittiin markkinointiautomaatioprosessi, joka sisällyttää olennaiset osa-alueet markkinointiautomaation harjoittamisessa. Tutkimusaineistoa käsitellään kahdeksan teemassa: asiakasdatan keräys, asiakasymmärrys, markkinointiautomaation rooli, asiakaspolku, näkemys markkinointisisällöstä, personoinnin vaikutus, yhteistyö asiakkaiden kanssa sekä markkinointiautomaatiosta saatu palaute. Tutkimusaineistossa ilmeni, kuinka kaikkikanavaisuuden hallinta korostui asiakasymmärryksen ja markkinointiautomaation osaamisessa. Tutkimusaineistossa ilmeni, kuinka markkinointiautomaation avulla voidaan löytää myös uusia asiayhteyksiä asiakkaista, mitä voidaan hyödyntää yritysten pitkän ajan strategian suunnittelussa. Aiempi tutkimus ja haastatteluaineisto vahvistavat päämäärällisen asiakaspolun huomioimista asiakasymmärryksessä, jotta asiakkaille voidaan tarjota arvoa paremman laatuisten tuotteiden ja sisältömarkkinoinnin muodoissa, jotta asiakas onnistuu saavuttamaan haluamansa päämäärät palveluntarjoajan avulla. Tämä vaatii asiakkaan oikeaa puhuttelua personoinnin muodossa, mutta hänen yksityisyytensä tarpeellista huomioimista. Asiantuntijoiden operatiivisiin suosituksiin lukeutuivat hyödyllinen jatkoviestintä ostettujen tuotteiden huolto-ohjeiden muodossa, sekä kehittämällä markkinointiautomaatiolla ominaisuuksia, joilla asiakas voi jatkaa asiakkuuttaan helpommin asiakaskokemuksen ja asiakaspolun ylläpitämiseksi. Tutkimuksen haastatteluhavaintoihin lukeutuvat, kuinka yhteistyö asiakkaiden kanssa markkinointiautomaatioprosessin kehittämisessä on mahdollinen kartoittamaton kilpailukeinon muoto, asiakaspalaute markkinointiautomaatiosta voi toimia laatukyvykkyyden mittarina ja kuinka asiakaspalautteen kerääminen dialogin muodossa on ollut onnistunut asiakasymmärryksen lähde. Tämä tutkielma kehottaa markkinointiautomaatioprosessin, päämääräisen asiakaspolun, palveluntarjoajan ja kuluttajien välisen vuoropuhelun hallinnan, jotta arvon yhteisluonti toteutuu paremmin

O impacto da inteligência artificial no negócio eletrónico

Pela importância que a Inteligência Artificial exibe na atualidade, revela-se de grande interesse verificar até que ponto ela está a transformar o Negócio Eletrónico. Para esse efeito, delineou-se uma revisão sistemática com o objetivo de avaliar os impactos da proliferação destes instrumentos. A investigação empreendida pretendeu identificar artigos científicos que, através de pesquisas realizadas a Fontes de Dados Eletrónicas, pudessem responder às questões de investigação implementadas: a) que tipo de soluções, baseadas na Inteligência Artificial (IA), têm sido usadas para melhorar o Negócio Eletrónico (NE); b) em que domínios do NE a IA foi aplicada; c) qual a taxa de sucesso ou fracasso do projeto. Simultaneamente, tiveram de respeitar critérios de seleção, nomeadamente, estar escritos em inglês, encontrarem-se no intervalo temporal 2015/2021 e tratar-se de estudos empíricos, suportados em dados reais. Após uma avaliação de qualidade final, procedeu-se à extração dos dados pertinentes para a investigação, para formulários criados em MS Excel. Estes dados estiveram na base da análise quantitativa e qualitativa que evidenciaram as descobertas feitas e sobre os quais se procedeu, posteriormente, à sua discussão. A dissertação termina com as conclusão e discussão de trabalhos futuros.Due to the importance that Artificial Intelligence exhibits today, it is of great interest to see to what extent it is transforming the Electronic Business. To this end, a systematic review was designed to evaluate the impacts of the proliferation of these instruments. The research aimed to identify scientific articles that, through research carried out on Electronic Data Sources, could answer the research questions implemented: a) what kind of solutions, based on Artificial Intelligence, have been used to improve the Electronic Business; b) in which areas of the Electronic Business Artificial Intelligence has been applied; c) what the success rate or failure of the project is. At the same time, they must comply with selection criteria, to be written in English, to be found in the 2015/2021-time interval and to be empirical studies supported by actual data. After a final quality evaluation, the relevant data for the investigation were extracted for forms created in MS Excel. These data were the basis of the quantitative and qualitative analysis that evidenced the findings found and on which they were subsequently discussed. The dissertation ends with the conclusion and discussion of future works

Extensión del modelo de comunicación publicitaria de Rossiter y Percy a la decisión de compra electrónica del consumidor

El modelo de Rossiter y Percy (1985,1987) se ha utilizado como marco conceptual ampliamente en investigaciones sobre comunicación publicitaria, mientras que su aplicación en el contexto de la decisión de compra es muy limitada. Esta Tesis propone la validación del modelo para explicar las ventas online de múltiples categorías de producto según las características de la comunicación empleadas en el comercio electrónico. También plantea a través de las dimensiones del modelo,estudiar cómo las características de la atmósfera del comercio electrónico y el nivel de familiaridad del consumidor pueden afectar la decisión de compra.The Rossiter and Percy model (1985, 1987) has been widely used as a conceptual framework in research on advertising communication, while its application in the context of purchase decision is very limited. This Thesis proposes the validation of the model to explain online sales of multiple product categories according to the communication characteristics used in e-commerce. The Thesis also propose studying how the characteristics of the e-commerce atmosphere and the level of consumer familiarity can affect the purchase decision through the dimensions of the model