Machine Learning Approach for Targeting and Recommending a Product for Project Management
Abstract
:1. Introduction
2. Methodology and Proposed Approach
- (1)
- PART-1: Acquisition of the review dataset of the market related to a brand/product;
- (2)
- PART-2: Import of the intelligent data analytics libraries;
- (3)
- PART-3: Review data analysis to know more about the dataset;
- (4)
- PART-4: Data pre-processing;
- (5)
- PART-5: Machine learning model formulation;
- (6)
- PART-6: AI/ML model development to select the informative and/or non-informative information and recommendation;
- (7)
- PART-7: Model validation and its performance visualization.
- (1)
- Modelling at input-layer for nth neurons:
- (2)
- Modelling at hidden-layer for hth neurons:
- (3)
- Modeling at output-layer for fth neurons:
3. Results Demonstration and Discussion
3.1. Dataset Used for the Performance Demonstration
3.2. Case Study: Analyzing the Cellphone Market Status
- -
- Seven brands (Apple, ASUS, Google, Motorola, Samsung, Sony, and Xiaomi) have a maximum rating of 5.
- -
- Four brands (Apple, Motorola, OnePlus, and Samsung) have a minimum rating of 1.
- -
- Google cell phone (Google Pixel XL, Quite Black 32GB) has a maximum number of reviews of 984.
- -
- The brand that has the maximum number of reviews is Samsung with 41,660.
- -
- The Samsung brand has a maximum number of items in the market with 397.
- -
- The maximum value of the “rating/review” ratio tells about the good brand that can be recommended. The following brands have maximum ratios: Samsung, Sony, Xiaomi, ASUS, Motorola, and Google.
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Reference and Year | Type of Application | Main Contribution and Characteristics | Analyzed Method/Remarks | Recommender System/Similarity with Our Approach/ |
---|---|---|---|---|
[4], 2000 | Analysis of CNs identify the new product opportunity | Analyze the train passenger’s experience for 6 domains, i.e., service, journey-time, connections, furnishing, catering, and communications equipment | QFD based approach. Process has traditionally been studied into two different domains | Manufacturing and engineering/Different |
[5], 1986 | CNs fulfilment based on strategic positioning | Approach is based on sequential process of selecting, introducing, elaborating, and fortifying a brand concept. | BCM (Brand Concept Management) based normative framework | Normative framework/Different |
[6], 2001 | CNs analysis enhance the new product design | State-of-the-art on PDD (product development decision) to show the academic contribution in Marketing, Organizations, Engineering Design, and Operations Management | Focused work is based on product development projects within a single firm | 1988-1998 conventional approaches/Different (only state-of-the-art) |
[7], 1986 | It is developed at Mitsubishi’s Kobe shipyard, then brought to USA by Ford and Xerox and then adopted by Japan, EU, USA firms | QFD (Quality Function Development) is based design, focusing on customer languages | Conventional QFD/Different | |
[8], 2019 | methods are supported in part by a long tradition of research and practice | Approach blends the perspectives of marketing, design, and manufacturing into a single approach to product development | Long tradition of research and practice/Different | |
[9], 2008 | CNs help to manage the portfolios of the product | Product portfolio planning for high level customer needs. | Hypothesis based development of a platform for product portfolios using customer needs. | Outline platform for differentiating modules/Different |
[10], 2020 | CNs support to recognize the variables utilized in the conjoint analysis | Theoretical information with the basis establishment on a conjoint application. | Model based on conjoint analysis, i.e., traditional, adaptive, choice, partial profile choice, and its recommendations. | Conjoint Analysis for design and pricing/Different |
[11], 1998 | CNs investigation enhance the available products and its related services | Managerial implications and the consequences of the application for two different ski industry. | Conventional Kano’s model of customer satisfaction analysis | Quality function deployment/Different |
[12], 1984 | Investigation of CNs is useful for product development | subjective and objective based two aspects of quality analysis | Theory based model development for one-dimensional quality analysis | Theoretical analysis/Different |
[13], 2011 | Perform the state-of-the-art most commonly used methods as per Kano model to identify the future research | Analyze the quality attributes according to the Kano models and compare with others | Kano conventional approaches/Different (only state-of-the-art) |
Ref. and Year | Type of Application | Main Contribution and Characteristics | Analyzed Method/Remarks | Recommender System/Similarity with Our Approach/ |
---|---|---|---|---|
[14], 2004 | Quality function deployment (QFD) based identify customer solutions or product attributes that address CNs | QFD for the industries of service, software, construction industry | Chart based quality of demand, promoting analysis | QFD conventional method/Different |
[15], 2002 | QFD to map CNs for solution | Perform the state-of-the-art to analyze the QFD future development | Studied the several functional field of QFD, i.e., Product development, Quality management, Customer needs analysis, Product design, Planning, Engineering, Decision-making, management, Teamwork, timing, costing, etc. | QFD conventional method/Different (only State-of-the-art) |
[16], 2004 | CNs for product design, testing, product launch | Based on 20 interviewed only, analysis has been performed, which is very feeble number for validation | Qualitative and exploratory based investigation using B2B firm | Conventional method/Different |
[17], 1978 | CNs based attributes identification for conjoint analysis | Similar to [10], conjoint analysis for product market for private sector | Different algorithms for conjoint analysis | Conventional method/Different |
[18], 2016 | Benefit based conjoint analysis | Map the value of attribute w.r.t benefit for two studies (durable product and a household consumable) | Benefit based model/Different | |
[19], 2002 | Focused group-based review the input to identify the CNs manually | To analyze the management of a new service development program (NSP) in the financial service industry of 12 organization only. | Analyze the linear and parallel model for development process with different NSD stage | Service development process/Different |
[20], 2015 | Ethnographic based market research analysis for manufacturing and service sectors. Moreover, application to new product development is not well studied. Four cases of 30 customers each have been analyzed | Ethnographic market research-based study of tribal clusters | Ethnographic model/Different | |
[21], 2010 | Analyze seven methods related to QFD of design process specially the specification phase, concept development, and the prototyping | Studied mainly three methods of customer involvement such as design for customers, design with customers, and design by customers | TQM conventional method/Different (only state-of-the-art) | |
[22], 2016 | web interface-based entry of CNs | three common product areas were analyzed to measure the quality of need statements | Hypothesis based front-end, user-centered approach development | Hypotheses based conventional model/Different |
[23], 2015 | 402 customers based shared needs and contextual images-based analysis to check the influence user behavior | studied a large-scale need finding methods | Higher quantities of needs for a household/Different |
Ref. and Year | Type of Application | Main Contribution and Characteristics | Analyzed Method/Remarks | Recommender System/Similarity with Our Approach/ |
---|---|---|---|---|
[24], 2016 | Unstructured textual data (UTD)-based address of the managerial questions | Sentence based text analysis for review data instead of word based for hotel and restaurant industry only | Introduces a “bag-of-sentences”. Analyze the latent Dirichlet allocation (LDA) method. | Text analysis model/Different |
[25], 2012 | Introduce the state-of-the-art for UTD based analysis in the following areas: “Online Product Opinions: Incidence, Evaluation, and Evolution”, “Network Characteristics and the Value of Collaborative UGC”, “Evaluating Promotional Activities in an Online Two-Sided Market of UGC”, etc. | User-Generated Content (UGC) based impact analysis | Model for Household, travel, hotel and service industry/Different (only state-of-the-art) | |
[26], 2011 | Analyze UTD word groupings with linking them to sales, sentiment, or product ratings | Decomposing textual reviews into segments describing different product features for two different groups of products (digital cameras and camcorders) | Analyze the clustering techniques for different products. | Text mining model/Different |
[27], 2014 | Brand sentiment analysis using social media data from firms in two distinct industries | Development of Joint model specification for sentiment and venue format analysis | Sentiment analysis model for software/Different | |
[28], 2006 | Leveraging a missing rating to improve online recommendation systems for movie rating data | Develop a recommendation model (Joint Model for Ordinal Ratings and Binary Selection), which is more complex statistical model for making recommendations | Joint statistical model/Different | |
[29], 2011 | Word groupings identification for product discussion | Online review based digital cameras market analysis, which is highly depend on phrases (i.e., strings) | NLP based Analyze and visualize the market structure using customer feedback received online | Text mining model/Different |
[30], 2012 | Performed a text mining for two case–sedan cars and diabetes Drugs. | Develop web-based software “a sonar” | Text mining sonar model/Different | |
[31], 2014 | Analyzed the 15 firms in five markets for market strategy. Models extract the latent dimensions of quality, which are computationally intensive | Analyzed an unified framework using unsupervised processing | LDA unsupervise model/Different | |
[32], 1993 | CNs mapping for product preference in a specific customer segment | QFD based product preference analysis using 200–400 customers need | QFD based model development. | QFD model/Different |
[33], 2012 | Engineering characteristics (ECs) based identification of product attributes for product development | Text mining for voice of customer (VOC) for business prioritization for product and service quality | Create a hybrid model for VOC data of Xerox Office Group. | Hybrid VOC model/Different |
[34], 2105 | Customer requirements in online reviews are manually translated into ECs for QFD of printer product | A probabilistic language analysis approach | QFD model/Different | |
[35], 2016 | Intangible attributes together with physical product attributes with supervised classification techniques | Analyzed a need-mining approach for e-mobility. Need further validation for application of other domain. | Develop a supervised learning algorithm. | Supervised model/Different |
Ref. and Year | Type of Application | Main Contribution and Characteristics | Analyzed Method/Remarks | Recommender System/Similarity with Our Approach/ |
---|---|---|---|---|
[36], 1995 | Online product configurator, design, conjoint analysis, voice of customer analysis, and affective engineering | Product analysis for classification, with high computational speed in a mathematical structure | Implementation of Kansei Engineering based on Ergonomics and computer science | KES model type-I, II, III/Different |
[37], 1998 | Analyze the literature survey related to product configuration | Review the model-based configuration system | Rule, model, case-based model/Different (only state-of-the-art) | |
[38], 2015 | CNs analysis based on opinions mining | Analyze a case study of Kindle Fire HD 7 in. tablet using a two-layer model. | Fuzzy support vector machines (SVMs) development for sentiment prediction | FSVM model/Different |
[39], 2017 | CNs analysis based on clustering and deep learning | NLP analysis using UGC data such as Kitchen Appliances, Skin Treatment, Prepared Foods, etc. | Design a convolutional neural Network (CNN) | CNN model/Different |
[40], 2016 | CNs analysis based on classification techniques | Micro blogs based UGC data based need-mining analysis using e-mobility Germany only dataset. | Machine Learning (ML) models (SVM, Bayes Net, Random Forests, DMNB, Naïve Bayes) to identify the particular posts, which express the needs. | ML model/Different |
[41], 2016 | Text-mining based on Italian restaurants dataset, collected from Yelp.com using NLP | QFD tool development using VOC concept. | VOC based QFD model/Different | |
[42], 2018 | Training and testing are performed based on previously labeled Twitter data of 1000 German e-mobility dataset. | Analyze the statistical classification using supervised learning-based algorithm (SVM) | SVM model/Different | |
[43], 2016 | Modern techniques-based recommendation systems for various product of the market | Analyze the fashion-aware personalized ranking system for women’s and man sneakers and clothing using Amazon dataset | Develop a one-class Filtering method using CNN | Filter method/Different |
[44], 2015 | Image-based analysis system for cloth and accessories | Analysis approach is based on Category Tree (CT) and Weighted Nearest Neighbor (WNN) | WNN model/Different | |
[45], 2020 | Analyze the e-commerce datasets to provide personalized product recommendations | Development of filtering algorithms | Filter method/Different | |
[46], 2018 | Performed a sequential recommendation analysis and compare the performance with 12 different models i.e., PopRec, BPR-MF, FMC, FPMC, PRME, HRMavg, HRMmax, TransRec, CatCos, FM, FMtime, FMcontent, etc. | Develop a TransFM model to analyze the Sequential recommendation | TransFM model/Different | |
[47], 2017 | Present a translation-based recommendation which is easy handle to large sequences and compare with different models similar to [46] | Develop a TransRec (Translation based Recommendation) model for third-order relationships | TransRec model/Different | |
[48], 2018 | Analyze the Self-Attentive Sequential Recommendation and compare with CNN and RNN-based models, which is highly memory dependent like [43,44,45,46,47,48]. | Develop a SASRec (self-attention based sequential) model similar to RNN (Recurrent Neural Network) using Markov Chains | SASRec model/Different | |
[49], 2018 | Analysis is based on book reviews dataset of Amazon in two implementations (uniform and stagewise for sampling and sampling strategies respectively) | Developed a chainRec algorithm along with optimization criterion | ChainRec model/Different | |
[50], 2017 | Analyzed the recommendation for bundle items for the large discount rate using 615 bundles of Australian Community Dataset. | Developed a Bayesian Personalized Ranking (BPR) model for bundle ranking | BPR model/Different | |
[51], 2019 | Analyzed the scene-based product recommendation for fashion compatibility using CTL datasets. | Analyzed the CNN model for both globally and locally compatibility. | CNN model/Different | |
[52], 2018 | Analyzed the product size recommendations and prediction of fitting using two datasets of ModCloth and RentTheRunWay | Analyzed the LMNN (Large Margin Nearest Neighbor) based kNN model | kNN model/Different |
Brand Name | Number of Items | Rating Analysis of the Brands | Total Reviews | |||||
---|---|---|---|---|---|---|---|---|
Min Value | Max Value | Mean Value | STD | Variance | Median | |||
Apple | 101 | 1 | 5 | 3.5277 | 0.5876 | 0.345224 | 3.6 | 11,922 |
ASUS | 13 | 1.8 | 5 | 3.7769 | 0.9212 | 0.84859 | 4 | 504 |
33 | 2 | 5 | 3.7636 | 0.5367 | 0.288011 | 3.7 | 4029 | |
HUAWEI | 36 | 2.6 | 4.8 | 4.0194 | 0.5236 | 0.274183 | 4.2 | 2972 |
Motorola | 100 | 1 | 5 | 3.528 | 0.7190 | 0.516986 | 3.6 | 8815 |
Nokia | 49 | 2.4 | 3.8 | 3.3224 | 0.3216 | 0.103444 | 3.4 | 5754 |
OnePlus | 7 | 1 | 4.2 | 3.3428 | 1.2246 | 1.499524 | 3.9 | 563 |
Samsung | 397 | 1 | 5 | 3.5733 | 0.6848 | 0.468932 | 3.6 | 41,660 |
Sony | 29 | 2 | 5 | 3.7310 | 0.5727 | 0.327931 | 3.8 | 3384 |
Xiaomi | 27 | 3.8 | 5 | 4.3370 | 0.2691 | 0.072422 | 4.4 | 2948 |
All cellphones | 792 | 1 | 5 | 3.6076 | 0.6687 | 0.447199 | 3.7 | 82,551 |
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Malik, H.; Afthanorhan, A.; Amirah, N.A.; Fatema, N. Machine Learning Approach for Targeting and Recommending a Product for Project Management. Mathematics 2021, 9, 1958. https://doi.org/10.3390/math9161958
Malik H, Afthanorhan A, Amirah NA, Fatema N. Machine Learning Approach for Targeting and Recommending a Product for Project Management. Mathematics. 2021; 9(16):1958. https://doi.org/10.3390/math9161958
Chicago/Turabian StyleMalik, Hasmat, Asyraf Afthanorhan, Noor Aina Amirah, and Nuzhat Fatema. 2021. "Machine Learning Approach for Targeting and Recommending a Product for Project Management" Mathematics 9, no. 16: 1958. https://doi.org/10.3390/math9161958
APA StyleMalik, H., Afthanorhan, A., Amirah, N. A., & Fatema, N. (2021). Machine Learning Approach for Targeting and Recommending a Product for Project Management. Mathematics, 9(16), 1958. https://doi.org/10.3390/math9161958