scholarly journals Personality or Value: A Comparative Study of Psychographic Segmentation Based on an Online Review Enhanced Recommender System

2019 ◽  
Vol 9 (10) ◽  
pp. 1992 ◽  
Author(s):  
Hui Liu ◽  
Yinghui Huang ◽  
Zichao Wang ◽  
Kai Liu ◽  
Xiangen Hu ◽  
...  
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Recommender System ◽  
Consumer Preferences ◽  
Explanatory Power ◽  
Marketing Research ◽  
User Preferences ◽  
User Preference ◽  
Data Driven ◽  
Product Categories

Big consumer data promises to be a game changer in applied and empirical marketing research. However, investigations of how big data helps inform consumers’ psychological aspects have, thus far, only received scant attention. Psychographics has been shown to be a valuable market segmentation path in understanding consumer preferences. Although in the context of e-commerce, as a component of psychographic segmentation, personality has been proven to be effective for prediction of e-commerce user preferences, it still remains unclear whether psychographic segmentation is practically influential in understanding user preferences across different product categories. To the best of our knowledge, we provide the first quantitative demonstration of the promising effect and relative importance of psychographic segmentation in predicting users’ online purchasing preferences across different product categories in e-commerce by using a data-driven approach. We first construct two online psychographic lexicons that include the Big Five Factor (BFF) personality traits and Schwartz Value Survey (SVS) using natural language processing (NLP) methods that are based on behavior measurements of users’ word use. We then incorporate the lexicons in a deep neural network (DNN)-based recommender system to predict users’ online purchasing preferences considering the new progress in segmentation-based user preference prediction methods. Overall, segmenting consumers into heterogeneous groups surprisingly does not demonstrate a significant improvement in understanding consumer preferences. Psychographic variables (both BFF and SVS) significantly improve the explanatory power of e-consumer preferences, whereas the improvement in prediction power is not significant. The SVS tends to outperform BFF segmentation, except for some product categories. Additionally, the DNN significantly outperforms previous methods. An e-commerce-oriented SVS measurement and segmentation approach that integrates both BFF and the SVS is recommended. The strong empirical evidence provides both practical guidance for e-commerce product development, marketing and recommendations, and a methodological reference for big data-driven marketing research.

A Method of Interest Degree Mining Based on Behavior Data Analysis

2019 ◽  
Vol 34 (09) ◽  
pp. 2059030
Author(s):  
Zhen Li ◽  
Shuo Xu ◽  
Tianyu Wang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Fusion ◽  
Language Processing ◽  
User Behavior ◽  
Comprehensive Evaluation ◽  
Mapping Method ◽  
User Preference ◽  
User Interest ◽  
Improvement Measures

Based on big data, this paper starts from the behavior data of users on social media, and studies and explores the core issues of user modeling under personalized services. Focusing on the goal of user interest modeling, this paper proposes corresponding improvement measures for the existing interest model, which has great difference in interest description among different users and it is difficult to find the user interest change in time. For the above problems, this paper takes user-generated content and user behavior information as the analysis object, and uses natural language processing, knowledge warehouse, data fusion and other methods and techniques to numerically analyze user interest mining based on text mining and multi-source data fusion. We propose a user interest label space mapping method to avoid data sparse problem caused by too many dimensions in interest analysis. At the same time, we propose a method to extract and blend the long-term and short-term interests, and realize the comprehensive evaluation of interests. In the analysis of the big data phase, the user preference social property application preference value law, it is expected to achieve user Internet social media application preference data mining from the perspective of big data.

Big Data-driven Electricity Plan Recommender System

2018 ◽  
Author(s):  
Yuan Zhang ◽  
Weicong Kong ◽  
Zhao Yang Dong ◽  
Ke Meng ◽  
Jin Qiu
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Data Driven

scholarly journals The paradigm of international marketing in the digital age

2021 ◽  
pp. 147-152
Author(s):  
A. T. Yerimpasheva ◽  
R. E. Tarakbaeva ◽  
S. A. Yolcu
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Consumer Preferences ◽  
Online Advertising ◽  
International Marketing ◽  
Marketing Research ◽  
Digital Age ◽  
Marketing Strategies ◽  
New Paradigm ◽  
Asymmetry Of Information

As globalization and the internationalization of economies develop, traditional marketing strategies are gradually fading into the background. The digital age is coming, which is forming a new paradigm of international marketing. At the same time, as a result of the COVID–19 pandemic, the processes of transition to digitalization have accelerated. The new paradigm of international marketing is manifested in the intensification of competition, frequent changes in the product range, the need to expand partnerships and the reduction of asymmetry of information. In order to attract and retain customers in the era of advanced digital technologies, successful companies are forced to develop new strategies. New technologies such as Big Data and artificial intelligence are becoming an alternative. Consumer preferences are also changing regarding the form of advertising. Online advertising becomes preferable. With the aim of to identify the main features of the new marketing paradigm, preliminary qualitative secondary and primary studies were conducted. To study secondary information, a search for scientific literature on the research topic was carried out in the databases SCOPUS, Science Direct and Springer, which allowed us to understand the main trends in the development of international marketing in the era of digitalization. To conduct primary research, we compiled a questionnaire, consisted of open-ended questions. The survey was conducted using a Google Form. The questionnaire contained four sections on the following topics: (I) Manifestations of a new marketing paradigm; (II) Marketing strategies in a digital environment; (III) Big data VS Marketing research; and (IV) Online Advertising. A sample of convenience, based on 12 respondents – marketing specialists, allowed formulating marketing strategies in the context of the digitalization of the world.

scholarly journals Dynamic Explainable Recommendation Based on Neural Attentive Models

2019 ◽  
Vol 33 ◽  
pp. 53-60 ◽  
Author(s):  
Xu Chen ◽  
Yongfeng Zhang ◽  
Zheng Qin
Keyword(s):  
Recommender System ◽  
User Modeling ◽  
User Preferences ◽  
User Preference ◽  
Crowd Sourcing ◽  
Product Features ◽  
Current State ◽  
Sentence Level ◽  
Gated Recurrent Unit ◽  
Time Aware

Providing explanations in a recommender system is getting more and more attention in both industry and research communities. Most existing explainable recommender models regard user preferences as invariant to generate static explanations. However, in real scenarios, a user’s preference is always dynamic, and she may be interested in different product features at different states. The mismatching between the explanation and user preference may degrade costumers’ satisfaction, confidence and trust for the recommender system. With the desire to fill up this gap, in this paper, we build a novel Dynamic Explainable Recommender (called DER) for more accurate user modeling and explanations. In specific, we design a time-aware gated recurrent unit (GRU) to model user dynamic preferences, and profile an item by its review information based on sentence-level convolutional neural network (CNN). By attentively learning the important review information according to the user current state, we are not only able to improve the recommendation performance, but also can provide explanations tailored for the users’ current preferences. We conduct extensive experiments to demonstrate the superiority of our model for improving recommendation performance. And to evaluate the explainability of our model, we first present examples to provide intuitive analysis on the highlighted review information, and then crowd-sourcing based evaluations are conducted to quantitatively verify our model’s superiority.

Triggers of positive eWOM: exploration with web analytics

2019 ◽  
Vol 37 (4) ◽  
pp. 433-450 ◽  
Author(s):  
Sultan Amed ◽  
Srabanti Mukherjee ◽  
Prasun Das ◽  
Biplab Datta
Keyword(s):  
Big Data ◽  
Real Time ◽  
Language Processing ◽  
Product Type ◽  
Support Vector ◽  
Product Packaging ◽  
Online Retail ◽  
Content Type ◽  
Product Categories ◽  
Online Purchase

Purpose The purpose of this paper is to determine the triggers of positive electronic word of mouth (eWOM) using real-time Big Data obtained from online retail sites/dedicated review sites. Design/methodology/approach In this study, real-time Big Data has been used and analysed through support vector machine, to segregate positive and negative eWOM. Thereafter, using natural language processing algorithms, this study has classified the triggers of positive eWOM based on their relative importance across six product categories. Findings The most important triggers of positive eWOM (like product experience, product type, product characteristics) were similar across different product categories. The second-level antecedents of positive eWOM included the person(s) for whom the product is purchased, the price and the source of the product, packaging and eagerness in patronising a brand. Practical implications The findings of this study indicate that the marketers who are active in the digital forum should encourage and incentivise their satisfied consumers to disseminate positive eWOM. Consumers with special interest for any product type (mothers or doctors for baby food) may be incentivised to write positive eWOM about the product’s ingredients/characteristics. Companies can launch the sequels of existing television or online advertisements addressing “for whom the product is purchased”. Originality/value This study identified the triggers of the positive eWOM using real-time Big Data extracted from online purchase platforms. This study also contributes to the literature by identifying the levels of triggers that are most, more and moderately important to the customers for writing positive reviews online.

A Weighted Method to Update Network User Preference Profile Dynamically

2014 ◽  
Vol 6 (2) ◽  
pp. 52-67 ◽  
Author(s):  
Zhi-Yuan Zhang ◽  
Yun Liu ◽  
Qing-An Zeng
Keyword(s):  
Recommender System ◽  
User Preferences ◽  
Preference Profile ◽  
User Preference ◽  
Adaptation Process ◽  
Initial Profile ◽  
Personal Preference ◽  
Numerical Attributes ◽  
Challenging Tasks

There are many alternatives in a Recommender System (RS) that can be represented by numerical attributes. One of the most challenging tasks in developing RS is the design of techniques that can infer user preferences through observation of their actions. A RS usually stores a personal preference profile associated with each user, but the initial profile of a user is usually incomplete and imprecise. Therefore, it is necessary to update a user's preference profile dynamically. Some previous research has covered this area, but neglected an important fact in real situations, where different weights should be considered for every attribute when selecting alternatives and updating a user preference profile. This paper provides a realistic and weighted method to update network user preferences through analysis of user selections. More specifically, an algorithm to compute and update weights of different attributes in a dynamic way is presented. The weights are used in the adaptation process of network user preference profile. The method is tested by extensive simulations and the obtained results show that it is more effective than previous methods.

scholarly journals On the Smaller Number of Inputs for Determining User Preferences in Recommender Systems

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2138
Author(s):  
Sang-Min Choi ◽  
Dongwoo Lee ◽  
Chihyun Park
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
Magic Number ◽  
User Profile ◽  
User Preferences ◽  
User Preference ◽  
Minimum Number ◽  
Movie Recommendation ◽  
Content Based Filtering

One of the most popular applications for the recommender systems is a movie recommendation system that suggests a few movies to a user based on the user’s preferences. Although there is a wealth of available data on movies, such as their genres, directors and actors, there is little information on a new user, making it hard for the recommender system to suggest what might interest the user. Accordingly, several recommendation services explicitly ask users to evaluate a certain number of movies, which are then used to create a user profile in the system. In general, one can create a better user profile if the user evaluates many movies at the beginning. However, most users do not want to evaluate many movies when they join the service. This motivates us to examine the minimum number of inputs needed to create a reliable user preference. We call this the magic number for determining user preferences. A recommender system based on this magic number can reduce user inconvenience while also making reliable suggestions. Based on user, item and content-based filtering, we calculate the magic number by comparing the accuracy resulting from the use of different numbers for predicting user preferences.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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