A novel approach to alleviate data sparsity and generate dynamic fruit recommendations from point‐of‐sale data

Abstract

Summary Recommender systems have become a core part of the retail experience. Retailers often rely on recommender systems to help them drive more conversions through targeted communication and advertisements. However, recommender systems are not one size fits all. Specialized retailers require specialized recommender systems to consider various features, attributes, and dynamics about the product category. In this paper, we have proposed a novel fruit recommender system that generates dynamic recommendations while remediating the problem of data sparsity. We have developed a novel fruit recommender system that considers the temporal dynamics in the fruit market, like price fluctuations, fruit seasonality, and quality variations that occur throughout the year. To perform this task, we have used Recurrent Recommender Network (RRN), which uses the deep learning method Long Short‐Term Memory (LSTM) to implement the system model. To ensure that our work and results obtained are practical, we have worked in a real‐world setting, by tying up with a specialty fruit retailer based in New Delhi to get the real‐world Point‐of‐Sale (POS) data of consumers. The result of the study suggests our algorithm performs better than other benchmark algorithms along NDCG and RMSE metrics.

A taxonomy and survey of big data in social media

“…This process, known as social listening, offers valuable insights into customer needs, expectations, and emerging trends. By understanding public perception, companies can make informed decisions, address concerns, and identify areas for improvement 39,54 …”

“…By understanding public perception, companies can make informed decisions, address concerns, and identify areas for improvement. 39,54 Influencer marketing: Big data assists in identifying important persons on social media who have a substantial influence on their followers' thoughts and purchase decisions. Companies can find influencers aligned with their brand and target audience through data analysis.…”

“…Gupta et al 54 introduced an innovative approach to address the issue of limited data availability and generate personalized fruit recommendations dynamically using point‐of‐sale data. By devising a novel method, the research aimed to overcome the scarcity of data for recommending fruits to customers.…”

Research on Fruit Recommendation Scheme Based on Delphi Hierarchical Analysis

“…Xiao Qian, a scholar, designed a recommendation algorithm and finally realized a mixed recommendation system for fruit matching based on user behavior characteristics, and the results showed that it could effectively improve the quality of fruit matching recommendation [5] . Gupta Garima, a foreign scholar, proposed a fruit recommendation system using a deep learning approach that takes into account the temporal dynamics of the fruit market, such as price fluctuations, fruit seasonality and quality changes that occur throughout the year, in order to improve the sales of retailers [6] . A synthesis of the literature shows that none of the existing studies have proposed fruit recommendations based on nutritional composition for specific populations.…”