scispace - formally typeset
Search or ask a question

What is Recommender System? 

Best insight from top research papers

A recommender system is a type of information filtering system that provides personalized suggestions to users based on their preferences and behavior. It helps users make decisions by recommending items that are most relevant to them, such as products to purchase, music to listen to, or news to read. Recommender systems can be used in various domains, including online stores, social media platforms, and research articles. There are different types of recommender systems, including content-based filtering, collaborative filtering, and hybrid approaches. Content-based filtering recommends items based on the attributes of the items themselves, while collaborative filtering suggests items based on the behavior and preferences of similar users. Hybrid approaches combine multiple techniques to provide more accurate recommendations. Evaluating recommender systems and addressing challenges in algorithmic requirements are ongoing areas of research .

Answers from top 4 papers

More filters
Papers (4)Insight
Open accessPosted ContentDOI
06 Feb 2023
The paper provides an overview of recommender systems, which are personalized recommendation systems used in online services to suggest items to users based on their preferences and behavior.
The paper is about recommendation systems, which are critical tools used by marketing departments and data scientists to provide customers with product recommendations based on their purchase history and purchasing behavior.
The paper provides a general definition of a recommendation system as a type of machine learning algorithm that suggests items, products, or services to users based on their preferences, past behavior, and interests.
Recommender systems are information filtering systems that provide suggestions for items most relevant to a user, such as products, music, or news.

Related Questions

Recommendation system Explanation ?5 answersRecommendation systems are tools used by marketing departments and data scientists to provide product suggestions based on customer behavior and purchase history. There are two types of recommendation systems: content-based and collaborative filtering. Content-based recommendations are based on the customer's purchase history, while collaborative filtering suggests products based on purchasing behavior. Collaborative filtering can be divided into content-based filtering, which suggests products based on similar purchasing behavior, and item-based filtering, which suggests products based on their attributes. Various methods such as user-based and item-based nearest-neighbor collaborative filtering and probabilistic methods are used to analyze data and provide product suggestions. Explainable recommender systems have also gained attention, as they provide justification for recommendations and increase users' trust. These systems generate reliable explanations with finer granularity and incorporate user and item profiles into semantic representation. They also enable personalization of the generated sentence and significantly outperform existing methods in generating explanations.
What is methodology recommendation system?4 answersA methodology for recommendation systems involves predicting items that users are likely to be interested in based on their past interactions. Existing recommendation methods have limitations in modeling complete user preferences and considering the process consistency of user behavior. One proposed method is based on the Entity Interaction Knowledge Graph (EIKG), which uses collaborative filtering and user behavior similarity to recommend items. Another approach is a multi-task feature learning recommendation model that incorporates time intervals and knowledge graph embedding to improve recommendation accuracy. Additionally, a context-aware recommendation system uses attributes to filter items and a mathematical model has been developed for this purpose. Trust relationships among users have also been studied to enhance user-based recommender systems, with an implicit approach for constructing a trust-network and a recommendation strategy based on predicted ratings and user scores.
What is recommendation system?4 answersA recommendation system is a collection of algorithms that learn from data and make suggestions to users based on their preferences and behavior. These systems are widely used in various industries such as e-commerce, entertainment, and online shopping to provide personalized recommendations for items like movies, products, and restaurants. They can be content-based, which uses the user's purchase history or attributes of items to make recommendations, or collaborative filtering, which suggests items based on similar user behavior or preferences. Collaborative filtering can further be divided into user-based and item-based filtering, as well as probabilistic methods. The goal of recommendation systems is to improve customer satisfaction, increase revenue, and assist users in finding relevant and interesting items.
What is system recommendation?5 answersA recommendation system is an information filtering system that utilizes algorithms to predict a user's preferences or ratings for a particular item and recommend new relevant content to the user. These systems are widely used in various fields such as e-commerce, banking, insurance, transportation, biology, and medicine. They are used to suggest products, social media content, web content, music, news, books, research papers, and goods to users. Recommendation systems can be categorized into different types, including content-based, collaborative filtering, and hybrid approaches. Collaborative filtering is one of the most common methods used in recommendation systems. These systems leverage data filtering techniques and algorithms to filter redundant information from large datasets and provide personalized recommendations to users based on their preferences.
What are the different types of recommendation systems?4 answersRecommender systems are used to assist users in finding relevant products or services based on their preferences. The different types of recommendation systems mentioned in the abstracts include collaborative filtering, content-based filtering, demographic, hybrid, knowledge-based, and social-based recommendation systems. Collaborative filtering RS, content-based RS, and hybrid RS are the three main categories of recommendation systems discussed. Additionally, context-aware recommendation systems are mentioned as well. The abstracts also highlight the importance of scholarly recommendation systems in the domain of literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. Content-based filtering is commonly used in scholarly recommendation systems, while collaborative filtering is more popular in conference recommenders.
What is recommender systems in information retrieval?6 answers

See what other people are reading

What are relationship between hotel quality and price in Southeast Asia?
4 answers
The relationship between hotel quality and price in Southeast Asia is crucial for customer satisfaction and loyalty. Research indicates that service quality and price significantly impact customer satisfaction and repurchase intention. Additionally, the environment, room price, hotel location, and service quality collectively influence customer satisfaction in Southeast Asian countries. Moreover, the quality of service, price, and location play vital roles in attracting and retaining guests at hotels in the region. Therefore, maintaining a balance between offering high service quality and competitive pricing is essential for hotels in Southeast Asia to enhance customer loyalty and satisfaction, especially during challenging times like the current pandemic.
What is code charge studio?
5 answers
Code Charge Studio is a powerful development environment designed for creating and testing real-time embedded signal processing applications, particularly on Texas Instruments' high-performance DSP platforms. It offers a range of tools such as tuning tools for optimization, C/C++ compiler, real-time operating system, and data visualization capabilities to streamline DSP programming and application design processes. Additionally, Code Charge Studio is part of the Integrative AI framework proposed in the i-Code Studio, which aims to facilitate efficient model composition and coordination for complex multimodal tasks without the need for fine-tuning. The i-Code Studio leverages multiple pre-trained models in a flexible and composable manner to achieve impressive results in various zero-shot multimodal tasks like video-to-text retrieval and visual question answering.
Who are the most prominent scholars who have written about algorithmic trading?
5 answers
Prominent scholars who have extensively written about algorithmic trading include Alvaro Cartea, Sebastia..., Xinyi Cai, and Sahar D. Al-Sudani and Dhiya Al-Jumeily. Alvaro Cartea and colleagues have co-authored a volume on Algorithmic and High-Frequency Trading, showcasing their expertise in this field. Xinyi Cai's research delves into algorithmic trading strategies, particularly focusing on the impact of dynamic learning on profit and loss, emphasizing the importance of prior information for informed traders. Additionally, Sahar D. Al-Sudani and Dhiya Al-Jumeily have explored the development of unconventional trading algorithms that aim to eliminate psychological barriers and achieve significant returns on investments, offering a unique perspective on algorithmic trading beyond traditional high-frequency models.
How do adversarial attacks impact the performance and resilience of machine learning algorithms in network security?
5 answers
Adversarial attacks significantly impact the performance and resilience of machine learning algorithms in network security. These attacks aim to deceive ML models into making incorrect predictions, posing serious threats to systems like Network Intrusion Detection Systems (NIDS). Studies show that both traditional neural networks (NN) and quantum neural networks (QNN) are vulnerable to adversarial attacks, with QNNs exhibiting better precision and recall post-attack but experiencing a more significant accuracy drop compared to NNs. Continuous re-training, even without specific adversarial training, can mitigate the effects of such attacks on NIDS. The gap between research findings and real-world practicality underscores the need for robust defense mechanisms to enhance the security and resilience of ML algorithms in network security applications.
What is the definition of supply chain performance?
5 answers
Supply chain performance refers to the effectiveness and efficiency of the activities within a supply chain, encompassing aspects such as timely delivery of raw materials, meeting production expectations, and on-time product delivery to customers. It is a critical factor for organizational success, impacting competitiveness by minimizing wasteful resource utilization. Evaluating supply chain performance involves considering the entire chain's activities and information flow from suppliers to customers, rather than individual members. Performance measurement in the supply chain can be achieved through approaches like the SCOR model, which assesses activities from upstream to downstream, focusing on reliability, responsiveness, flexibility, cost, and assets. Implementing effective supply chain management practices is essential for enhancing organizational performance and profitability, making it a crucial competitive strategy in today's globalized world.
What is the relationship between prisons and private correctional healthcare in the U.S.?
5 answers
Private correctional healthcare in the U.S. has been a topic of interest due to its implications on service quality and inmate mortality. The privatization of correctional services, including healthcare, has been a significant trend since the 1980s, with companies like CoreCivic and GEO Group playing a major role. Studies have shown mixed results regarding the impact of privatization on inmate mortality, with some indicating potential increases in mortality associated with privatized correctional healthcare, while others suggest fewer per capita deaths in privatized prisons. Despite concerns about profit incentives leading to reduced healthcare services in privatized prisons, there is evidence of cost savings and good quality provision in privately run facilities, emphasizing the importance of public safeguards and monitoring to ensure service quality and security.
Why is python so popular in machine learning?
5 answers
Python is popular in machine learning due to its simplicity, efficiency, and extensive libraries supporting ML algorithms. Python's object-oriented approach, elegant syntax, and high-level data structures make it ideal for developing applications in various domains, including machine learning and data science. Additionally, Python's widespread adoption in the ML community is attributed to its versatility and ease of use, making it accessible for beginners and experts alike. Furthermore, Python's popularity in machine learning is reinforced by its application in developing ML models for various domains like fraud detection, self-driving cars, and recommendation systems. Despite concerns about Python's suitability for flight critical applications, it remains a top choice for developing machine learning models due to its flexibility and robust ecosystem.
How does Virginia Woolf's "Kew Gardens" reflect on the concept of anthropocentrism?
4 answers
Virginia Woolf's "Kew Gardens" challenges anthropocentrism by portraying human experiences alongside non-human elements like an invisible snail, highlighting the insignificance of human concerns in the grand scheme of existence. This defiance of anthropocentrism is further emphasized in Woolf's work "Flush," where she intertwines the Bildung processes of a dog and a woman to transcend traditional human-centered narratives. Woolf's broader body of work, including her short fiction, showcases a deep engagement with political themes and challenges the notion of human exceptionalism, advocating for a more inclusive perspective that acknowledges the entanglements between human and non-human experiences. Additionally, Woolf's exploration of animal subjectivity in her writings contributes to a growing discourse on the representation of animals in literature, aiming to break down the human/animal binary and question the extent of anthropocentrism in fictional portrayals of animals.
How does the implementation of digital twin technology impact the return on investment in supply chain management?
5 answers
The implementation of digital twin technology in supply chain management has a significant impact on the return on investment (ROI). Digital twins optimize real-world performance within a virtual context, enhancing SCM efficiency. They enable the development of better products, early issue detection, and more accurate outcome predictions, reducing costs and waste in manufacturing processes. By utilizing digital twins, logistics service providers can collaborate with manufacturers in e-commerce activities, leading to improved coordination and efficiency in distribution networks. Incorporating digital twins in the supply chain can contribute to sustainability by reducing energy consumption, travel time, and enhancing asset utilization. The use of digital twin technology, along with other advanced technologies, such as AI and big data analysis, enhances supply chain efficiency and effectiveness.
How can monitoring act as a signaling mechanism to suppliers?
5 answers
Monitoring can serve as a signaling mechanism to suppliers by influencing sustainability diffusion within supply networks. Research suggests that monitoring impacts sustainability diffusion at the dyadic level, while mentoring is crucial for diffusion at the supply network level. Additionally, private social auditors play a key role in identifying and reporting problems in supply chain factories, helping to mitigate reputational risks by reducing information asymmetries. Furthermore, companies are increasingly relying on monitoring coalitions to organize the monitoring of labor or other standards in overseas factories, highlighting the importance of effective monitoring processes in ensuring compliance with global labor and environmental standards. This integrated approach of monitoring and mentoring can facilitate the spread of sustainability practices beyond first-tier suppliers, emphasizing the significance of these strategies in enhancing supplier engagement and fostering sustainability diffusion in supply networks.
Is structured or unstructured form of interview best?
5 answers
Structured interviews are generally considered superior to unstructured interviews. Structured interviews involve standardized components like consistent questions and rating scales, leading to increased validity, reliability, reduced bias, and legal defensibility. In contrast, unstructured interviews lack this standardization, resulting in lower reliability and validity. Structured interviews are particularly favored in clinical settings for diagnosing mental disorders due to their improved reliability, validity, and content coverage compared to unstructured approaches. While unstructured interviews may allow for more flexibility, structured interviews are more effective in ensuring consistent and accurate data collection, making them the preferred choice in various fields.