From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations

• URL: http://arxiv.org/abs/2402.08109v1
• Date: Mon, 12 Feb 2024 22:56:18 GMT
• Title: From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations
• Authors: Kapilya Gangadharan, K. Malathi, Anoop Purandaran, Barathi Subramanian, and Rathinaraja Jeyaraj
• Abstract summary: This research aims to explore the impact of Machine Learning (ML) on the evolution and efficacy of Recommendation Systems (RS) The research identifies the increasing expectation of users for a seamless, intuitive online experience, where content is personalized and dynamically adapted to changing preferences.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research aims to explore the impact of Machine Learning (ML) on the evolution and efficacy of Recommendation Systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data sourcing, feature engineering, and the importance of evaluation metrics, thereby highlighting the iterative nature of enhancing recommendation algorithms. The deployment of Recommendation Engines (RE), driven by advanced algorithms and data analytics, is explored across various domains, showcasing their significant impact on user experience and decision-making processes. These engines not only streamline information discovery and enhance collaboration but also accelerate knowledge acquisition, proving vital in navigating the digital landscape for businesses. They contribute significantly to sales, revenue, and the competitive edge of enterprises by offering improved recommendations that align with individual customer needs. The research identifies the increasing expectation of users for a seamless, intuitive online experience, where content is personalized and dynamically adapted to changing preferences. Future research directions include exploring advancements in deep learning models, ethical considerations in the deployment of RS, and addressing scalability challenges. This study emphasizes the indispensability of comprehending and leveraging ML in RS for researchers and practitioners, to tap into the full potential of personalized recommendation in commercial business prospects.

Related papers

• A Survey on Reinforcement Learning Applications in SLAM [1.1682807230625691]
  This study explores the application of reinforcement learning in the context of SLAM. By enabling the agent (the robot) to iteratively interact with and receive feedback from its environment, reinforcement learning facilitates the acquisition of navigation and mapping skills. The findings of this study, which provide an overview of reinforcement learning's utilization in SLAM, reveal significant advancements in the field.
  arXiv  Detail & Related papers  (2024-08-26T00:13:14Z)
• Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives [40.62773366902451]
  This paper advocates a specific viewpoint on the field of process mining. We first summarize the framework of process mining, common industrial applications, and the latest advances combined with artificial intelligence. This particular perspective aims to revolutionize process mining by leveraging artificial intelligence to offer sophisticated solutions for complex, multi-organizational data analysis.
  arXiv  Detail & Related papers  (2024-07-15T23:30:34Z)
• A Survey of Latent Factor Models in Recommender Systems [0.0]
  This survey systematically reviews latent factor models in recommender systems. The literature is examined through a structured framework covering learning data, model architecture, learning strategies, and optimization techniques.
  arXiv  Detail & Related papers  (2024-05-28T11:28:59Z)
• An explainable machine learning-based approach for analyzing customers' online data to identify the importance of product attributes [0.6437284704257459]
  We propose a game theory machine learning (ML) method that extracts comprehensive design implications for product development. We apply our method to a real-world dataset of laptops from Kaggle, and derive design implications based on the results.
  arXiv  Detail & Related papers  (2024-02-03T20:50:48Z)
• EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems [82.76483989905961]
  Current Sequential Recommender Systems (SRSs) suffer from computational and resource inefficiencies. We develop the Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems (EASRec) EASRec introduces data-aware gates that leverage historical information from input data batch to improve the performance of the recommendation network.
  arXiv  Detail & Related papers  (2024-02-01T07:22:52Z)
• Knowledge Editing for Large Language Models: A Survey [51.01368551235289]
  One major drawback of large language models (LLMs) is their substantial computational cost for pre-training. Knowledge-based Model Editing (KME) has attracted increasing attention, which aims to precisely modify the LLMs to incorporate specific knowledge.
  arXiv  Detail & Related papers  (2023-10-24T22:18:13Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
  Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI) We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
  arXiv  Detail & Related papers  (2023-07-05T06:03:40Z)
• How Can Recommender Systems Benefit from Large Language Models: A Survey [82.06729592294322]
  Large language models (LLM) have shown impressive general intelligence and human-like capabilities. We conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
  arXiv  Detail & Related papers  (2023-06-09T11:31:50Z)
• Scaling up Search Engine Audits: Practical Insights for Algorithm Auditing [68.8204255655161]
  We set up experiments for eight search engines with hundreds of virtual agents placed in different regions. We demonstrate the successful performance of our research infrastructure across multiple data collections. We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time.
  arXiv  Detail & Related papers  (2021-06-10T15:49:58Z)
• Developing a Recommendation Benchmark for MLPerf Training and Inference [16.471395965484145]
  We aim to define an industry-relevant recommendation benchmark for theerferf Training andInference Suites. The paper synthesizes the desirable modeling strategies for personalized recommendation systems. We lay out desirable characteristics of recommendation model architectures and data sets.
  arXiv  Detail & Related papers  (2020-03-16T17:13:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.