Product Recommendations Using Textual Similarity Based Learning Models

Shrivastava, Rahul; Sisodia, Dilip Singh

doi:10.1109/iccci.2019.8821893

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…D. sha et al had proposed a fashion clothing feature extraction for analyzing various attributes, such as cloth pattern, sleeves, and collar using GIST, Fourier and Pyramid Histogram of Oriented Gradients (PHOG) feature extraction and experimented using the tmall fashion dataset with 8000 images [45]. In [46], the authors proposed a text-based image recommendation using bag-of-words and the Term frequency-Inverse document frequency (TF-IDF) approach to get similar product image recommendations. For this approach, the authors experimented on the amazon fashion image dataset and computed the Euclidean distance measure to fetch the top-N similar recommendations.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Unsupervised Machine Learning Approach for a Similar Image Recommender System for E-Commerce

Addagarla

Amalanathan

2020

Symmetry

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The recommender system is the most profound research area for e-commerce product recommendations. Currently, many e-commerce platforms use a text-based product search, which has limitations to fetch the most similar products. An image-based similarity search for recommendations had considerable gains in popularity for many areas, especially for the e-commerce platforms giving a better visual search experience by the users. In our research work, we proposed a machine-learning-based approach for a similar image-based recommender system. We applied a dimensionality reduction technique using Principal Component Analysis (PCA) through Singular Value Decomposition (SVD) for transforming the extracted features into lower-dimensional space. Further, we applied the K-Means++ clustering approach for the possible cluster identification for a similar group of products. Later, we computed the Manhattan distance measure for the input image to the target clusters set for fetching the top-N similar products with low distance measure. We compared our approach with five different unsupervised clustering algorithms, namely Minibatch, K-Mediod, Agglomerative, Brich, and the Gaussian Mixture Model (GMM), and used the 40,000 fashion product image dataset from the Kaggle web platform for the product recommendation process. We computed various cluster performance metrics on K-means++ and achieved a Silhouette Coefficient (SC) of 0.1414, a Calinski-Harabasz (CH) index score of 669.4, and a Davies–Bouldin (DB) index score of 1.8538. Finally, our proposed PCA-SVD transformed K-mean++ approach showed superior performance compared to the other five clustering approaches for similar image product recommendations.

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Section: Related Workmentioning

confidence: 99%

Probabilistic Unsupervised Machine Learning Approach for a Similar Image Recommender System for E-Commerce

Addagarla

Amalanathan

2020

Symmetry

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“…The utility functions in these methods obtain utility for each stakeholder from the top- n recommendation list (RL). Popular personalized top- n recommendation techniques (Hernando et al , 2016; Luo et al , 2012; Pujahari and Sisodia, 2020b) and textual content-based RS (Shrivastava and Sisodia, 2019) mainly focus on improving recommendation accuracy. The utility functions targeting only end-user preferences may achieve better accuracy but lacks novel and diverse item recommendations (Ge et al , 2010; Vargas and Castells, 2011).…”

Section: Introductionmentioning

confidence: 99%

Utility optimization-based multi-stakeholder personalized recommendation system

Shrivastava

Sisodia

Nagwani

2022

DTA

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PurposeIn a multi-stakeholder recommender system (MSRS), stakeholders are the multiple entities (consumer, producer, system, etc.) benefited by the generated recommendations. Traditionally, the exclusive focus on only a single stakeholders' (for example, only consumer or end-user) preferences obscured the welfare of the others. Two major challenges are encountered while incorporating the multiple stakeholders' perspectives in MSRS: designing a dedicated utility function for each stakeholder and optimizing their utility without hurting others. This paper proposes multiple utility functions for different stakeholders and optimizes these functions for generating balanced, personalized recommendations for each stakeholder.Design/methodology/approachThe proposed methodology considers four valid stakeholders user, producer, cast and recommender system from the multi-stakeholder recommender setting and builds dedicated utility functions. The utility function for users incorporates enhanced side-information-based similarity computation for utility count. Similarly, to improve the utility gain, the authors design new utility functions for producer, star-cast and system to incorporate long-tail and diverse items in the recommendation list. Next, to balance the utility gain and generate the trade-off recommendation solution, the authors perform the evolutionary optimization of the conflicting utility functions using NSGA-II. Experimental evaluation and comparison are conducted over three benchmark data sets.FindingsThe authors observed 19.70% of average enhancement in utility gain with improved mean precision, diversity and novelty. Exposure, hit, reach and target reach metrics are substantially improved.Originality/valueA new approach considers four stakeholders simultaneously with their respective utility functions and establishes the trade-off recommendation solution between conflicting utilities of the stakeholders.

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