Product recommendation system for small online retailers using association rules mining

Chen, Junnan; Miller, Courtney; Dagher, Gaby G.

doi:10.1109/idam.2014.6912673

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…Following this line of research, Chen et al [40] show how association rules can be exploited to devise a recommender system for small online retailers that typically have limited processing power to perform complex analyses. Similarly, Mican and Tomai [41] use association rules to model connections between the pages of a web site and suggest relevant content.…”

Section: Non-personalized Recommendersmentioning

confidence: 99%

When Personalization Is Not an Option: An In-The-Wild Study on Persuasive News Recommendation

et al. 2019

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Aiming at granting wide access to their contents, online information providers often choose not to have registered users, and therefore must give up personalization. In this paper, we focus on the case of non-personalized news recommender systems, and explore persuasive techniques that can, nonetheless, be used to enhance recommendation presentation, with the aim of capturing the user’s interest on suggested items leveraging the way news is perceived. We present the results of two evaluations “in the wild”, carried out in the context of a real online magazine and based on data from 16,134 and 20,933 user sessions, respectively, where we empirically assessed the effectiveness of persuasion strategies which exploit logical fallacies and other techniques. Logical fallacies are inferential schemes known since antiquity that, even if formally invalid, appear as plausible and are therefore psychologically persuasive. In particular, our evaluations allowed us to compare three persuasive scenarios based on the Argumentum Ad Populum fallacy, on a modified version of the Argumentum ad Populum fallacy (Group-Ad Populum), and on no fallacy (neutral condition), respectively. Moreover, we studied the effects of the Accent Fallacy (in its visual variant), and of positive vs. negative Framing.

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Section: Non-personalized Recommendersmentioning

confidence: 99%

When Personalization Is Not an Option: An In-The-Wild Study on Persuasive News Recommendation

et al. 2019

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“…Generative adversarial networks (GAN), named as RecommenderGAN [16], have been one of the frameworks to develop product recommendation systems that generates samples depending on the combination of viewed and bought products. When developing for small scaled retailers with limited dataset and processing power, GAN's and data or rule mining have proved to be of excellent capability [16][17][18].…”

Section: Recommendation Systemsmentioning

confidence: 99%

Enabling “Untact” Culture via Online Product Recommendations: An Optimized Graph-CNN based Approach

Shafqat

Byun

2020

Applied Sciences

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The COVID-19 pandemic is swiftly changing our behaviors toward online channels across the globe. Cultural patterns of working, thinking, shopping, and use of technology are changing accordingly. Customers are seeking convenience in online shopping. It is the peak time to assist the digital marketplace with right kind of tools and technologies that uses the strategy of click and collect. Session-based recommendation systems have the potential to be equally useful for both the customers and the service providers. These frameworks can foresee customer’s inclinations and interests, by investigating authentic information on their conduct and activities. Various methods exist and are pertinent in various situations. We propose a product recommendation system that uses a graph convolutional neural network (GCN)-based approach to recommend products to users by analyzing their previous interactions. Unlike other conventional techniques, GCN is not widely explored in recommendation systems. Therefore, we propose a variation of GCN that uses optimization strategy for better representation of graphs. Our model uses session-based data to generate patterns. The input patterns are encoded and passed to embedding layer. GCN uses the session graphs as input. The experiments on data show that the optimized GCN (OpGCN) was able to achieve higher prediction rate with around 93% accuracy as compared with simple GCN (around 88%).

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“…Hung 18 constructed an online recommendation system based on a modified product taxonomy and customer classification to identify customers' shopping behavior. Existing product recommendation systems have been diffusely applied in online retail stores, 19,20 hotel bookings, 21 and for online sales of products such as cell phones, 22,23 movies, 24 books, 25,26 and so on. Some additional issues in online product recommendation systems include cold‐start product recommendation, 27 location‐based recommendation agents, 28 and group recommendations, 29 to name just a few.…”

Section: Introductionmentioning

confidence: 99%

Power‐average‐operator‐based hybrid multiattribute online product recommendation model for consumer decision‐making

et al. 2021

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This study develops a power‐average‐operator‐based hybrid multiattribute online product recommendation model that considers the consumer's risk attitude to rank categoric product options as a complement to existing recommender systems. Online production recommendation plays a key role in the development of e‐commerce, and can greatly improve consumers' shopping experiences. However, few online shopping sites provide interactive decision aids for consumers such that they can articulate their preferences towards multiple selection attributes with the purpose of mitigating choice difficulty and improving decision quality. Additionally, consumers' risk attitudes to online shopping dramatically impact their product choices. In the model proposed in this paper, the risk attitude‐based power average (RAPA) operator is used to integrate the risk attitude of the decision‐maker into the information fusion process of multiple attribute decision‐making. Subsequently, the risk attitude function, with several basic types, is introduced to quantify the risk attitude of the decision‐maker for use in the RAPA operator. A proportional hesitant fuzzy 2‐tuple linguistic term set (PHF2TLTS) is constructed by incorporating a binary of linguistic information aiming to comprehensively analyze the hybrid product information. With a focus on the information fusion process, the proportional hesitant 2‐tuple linguistic RAPA operator and weighted proportional hesitant 2‐tuple linguistic RAPA operator are introduced to aggregate a given set of PHF2TLTSs. The validity of the proposed model is demonstrated using an illustrative example, a comparison with existing approaches and detailed explanations of the performance differences.

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Product recommendation system for small online retailers using association rules mining

Cited by 12 publications

References 9 publications

When Personalization Is Not an Option: An In-The-Wild Study on Persuasive News Recommendation

When Personalization Is Not an Option: An In-The-Wild Study on Persuasive News Recommendation

Enabling “Untact” Culture via Online Product Recommendations: An Optimized Graph-CNN based Approach

Power‐average‐operator‐based hybrid multiattribute online product recommendation model for consumer decision‐making

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