by jsendak | Jan 21, 2024 | AI
Effective recommendation systems rely on capturing user preferences, often
requiring incorporating numerous features such as universally unique
identifiers (UUIDs) of entities. However, the exceptionally high cardinality of
UUIDs poses a significant challenge in terms of model degradation and increased
model size due to sparsity. This paper presents two innovative techniques to
address the challenge of high cardinality in recommendation systems.
Specifically, we propose a bag-of-words approach, combined with layer sharing,
to substantially decrease the model size while improving performance. Our
techniques were evaluated through offline and online experiments on Uber use
cases, resulting in promising results demonstrating our approach’s
effectiveness in optimizing recommendation systems and enhancing their overall
performance.
Improving Recommendation Systems with Techniques to Address High Cardinality
Recommendation systems are integral to many applications and platforms, from e-commerce websites to streaming services. These systems rely on capturing user preferences to provide personalized recommendations, which often requires incorporating numerous features such as universally unique identifiers (UUIDs) of entities. However, the exceptionally high cardinality of UUIDs poses significant challenges for these systems.
One of the main challenges presented by high cardinality is model degradation. When a recommendation model has to handle a large number of UUIDs, the model performance can start to degrade. This is due to the sparsity of the data, with many UUIDs having limited or no interactions associated with them. As a result, the model struggles to accurately learn and make predictions based on these sparse features.
In addition to model degradation, high cardinality also increases the model size. Each UUID needs to be represented as a separate feature in the model, which grows exponentially as the number of distinct UUIDs increases. This can lead to memory and computational inefficiencies, making it challenging to scale recommendation systems.
To overcome these challenges, this paper presents two innovative techniques: a bag-of-words approach and layer sharing. The bag-of-words approach involves treating UUIDs as words and representing them as vectors. This allows the recommendation system to leverage techniques used in natural language processing, such as word embeddings, to capture semantic relationships between UUIDs. By doing so, the model can better generalize and make predictions based on similar UUIDs, even if they have limited interactions.
The second technique, layer sharing, reduces the model size by sharing layers across different UUIDs. Instead of creating separate layers for each UUID, the recommendation system can share lower layers that capture general features common to multiple UUIDs. This not only decreases the overall model size but also improves computational efficiency by reducing redundant computations.
The effectiveness of these techniques was evaluated through offline and online experiments on Uber use cases. The results showed promising improvements in both model size reduction and performance enhancement. By reducing model size, the techniques make it easier to handle high cardinality, allowing recommendation systems to scale more efficiently. Furthermore, by improving performance despite the sparsity of data, these techniques enable more accurate and relevant recommendations for users.
What is particularly noteworthy about these techniques is their multi-disciplinary nature. The bag-of-words approach borrows from natural language processing techniques, applying them to the problem of high cardinality in recommendation systems. This cross-pollination of ideas and methodologies between different domains can often lead to innovative solutions with significant impact.
In conclusion, the presented techniques offer valuable insights into addressing the challenge of high cardinality in recommendation systems. By utilizing a bag-of-words approach and layer sharing, these techniques optimize model size, improve performance, and enable more efficient scaling. Furthermore, they highlight the importance of multidisciplinary approaches in solving complex problems and driving innovation in various domains.
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by jsendak | Jan 10, 2024 | DS Articles
This post explains how data governance can help data scientists handle AI fatigue and build robust models.
Understanding AI Fatigue and Its Impact on Business
Artificial Intelligence (AI) has been a game-changer for businesses, automating processes, improving efficiency, and delivering insights that drive strategic decision-making. However, as the adoption of AI becomes more widespread, a new challenge has emerged – AI fatigue. AI fatigue refers to the exhaustion and frustration that can arise from the continuous and rapid integration of AI technologies into business operations. It can manifest in various ways, such as a decrease in user engagement, increased resistance to new AI implementations, and a decline in the perceived value of AI applications.
AI fatigue can have a significant impact on business. It can lead to a reduction in the return on investment for AI projects, as employees become less likely to fully utilize and leverage AI tools. This can also result in a decrease in productivity and an increase in errors, as employees may revert back to manual processes or older technologies that they are more comfortable with. Furthermore, AI fatigue can stall innovation and progress, as businesses may become hesitant to pursue new AI initiatives due to past experiences of fatigue and resistance from their workforce.
The impact of AI fatigue on businesses is not just limited to internal operations. It can also affect customer satisfaction and loyalty. For example, if a company’s customer service chatbot is not functioning optimally due to AI fatigue, customers may become frustrated with the lack of effective support and turn to competitors. In today’s digital age, where customers expect quick and personalized service, AI fatigue can be detrimental to a business’s reputation and bottom line.
In conclusion, understanding AI fatigue and its impact on business is crucial for organizations that aim to harness the full potential of AI. Recognizing the signs of AI fatigue and taking proactive steps to address it can help businesses avoid the negative consequences that come with it. As we’ll explore in the following sections, data governance plays a pivotal role in combating AI fatigue and ensuring the successful deployment of AI technologies.
The Role of Data Governance in AI Deployment
Data governance is the process of managing the access, usage, and security of data within an organization. In the context of AI deployment, data governance plays a critical role in ensuring that AI systems are operating optimally and delivering accurate, reliable insights. Without proper data governance, AI systems can become overwhelmed with inaccurate or low-quality data, leading to poor performance and increased AI fatigue.
One of the key aspects of data governance is data quality management. This involves implementing processes to ensure that data is accurate, complete, and consistent. By maintaining high-quality data, businesses can avoid feeding their AI systems with erroneous information, which can lead to incorrect conclusions and decision-making.
Data governance also involves data privacy and security management. With the increasing amount of sensitive information being processed by AI systems, it’s essential to have robust security measures in place to protect against data breaches and unauthorized access. This not only helps to prevent AI fatigue but also ensures compliance with data protection regulations.
Another important aspect of data governance is data access management. This includes setting up permission levels for different users within an organization. By controlling who has access to certain data, businesses can prevent unauthorized usage and reduce the risk of data misuse, which can contribute to AI fatigue.
Overall, data governance is a crucial component of AI deployment. By implementing effective data governance strategies, businesses can ensure that their AI systems are functioning at their best, delivering valuable insights, and avoiding the pitfalls of AI fatigue.
Key Data Governance Strategies to Combat AI Fatigue
Implementing key data governance strategies is vital to combating AI fatigue within an organization. One such strategy is to establish clear data governance policies and procedures. These policies should outline the roles and responsibilities of individuals within the organization, as well as the processes for managing and maintaining data quality. Clear policies help to ensure that everyone within the organization is on the same page and knows what is expected of them in terms of data management.
Another effective strategy is to invest in data governance tools and technology. These tools can automate many of the processes involved in data governance, such as data quality management, data privacy and security management, and data access management. Automation can help to reduce the burden on employees and decrease the risk of human error, which can contribute to AI fatigue.
“Data governance is not just about controlling data, it’s about enabling the organization to make better decisions.” – Unknown
Organizations should also focus on fostering a culture of data literacy within their workforce. Employees should be trained on the importance of data governance and how it impacts the performance of AI systems. A data-literate workforce is more likely to recognize the signs of AI fatigue and take proactive steps to address it.
- Establish clear data governance policies and procedures
- Invest in data governance tools and technology
- Foster a culture of data literacy within the workforce
Finally, it’s essential to continuously monitor and assess the performance of AI systems. This involves tracking metrics such as accuracy, efficiency, and user engagement. Monitoring allows organizations to identify any issues early on and make necessary adjustments before AI fatigue sets in.
By implementing these key data governance strategies, organizations can combat AI fatigue and ensure the successful deployment and utilization of AI technologies.
Success Stories: How Companies Overcame AI Fatigue with Data Governance
There are several success stories of companies that have overcome AI fatigue through effective data governance. For example, a financial services firm was struggling with AI fatigue as their AI system was constantly producing inaccurate risk assessments due to poor data quality. By implementing strict data governance policies and investing in data quality management tools, they were able to improve the accuracy of their AI system, leading to better decision-making and increased user engagement.
In another case, a healthcare provider was facing resistance from their staff in using an AI-powered diagnostic tool. The tool was not always providing reliable results, leading to frustration and a lack of trust in the system. Upon reviewing their data governance practices, they discovered that there were issues with data access management. By restricting access to certain data and ensuring that only relevant and high-quality data was being used by the AI system, they were able to improve its performance and regain the trust of their staff.
A well-known e-commerce company also faced challenges with their AI-driven product recommendation system. Customers were receiving irrelevant recommendations, leading to a decrease in sales and customer satisfaction. The company conducted a thorough assessment of their data governance practices and found that they could improve their data privacy and security management. By implementing more robust security measures and ensuring that customer data was being used responsibly, they were able to improve the accuracy of their recommendations and increase sales.
“Data governance is the unsung hero of successful AI deployment. It’s not always visible, but its impact is undeniable.” – Unknown
These success stories demonstrate the importance of data governance in overcoming AI fatigue. By focusing on data quality, privacy, security, and access management, companies can ensure that their AI systems are functioning optimally and delivering the desired results.
Analyzing AI Fatigue and the Role of Data Governance
Recently, it has come into light how data governance can support data scientists in managing AI fatigue and building robust models. This post will dissect these key points and provide constructive guidance based on these insights.
Understanding AI Fatigue
AI fatigue often occurs when an organization experiences diminishing returns on its AI projects, due to a variety of reasons such as underwhelming performance or complicated AI systems. Data management problems also play a vital role in influencing AI fatigue.
“Effective data management is critical for developing successful AI models. The lack of proper data governance can weigh down the performance of AI, ultimately leading to AI fatigue.”
The Relevance of Data Governance
Data governance comes into play by establishing standards for data quality, enforcing consistency in data management, and creating a more coherent structure of data that immensely benefits AI models.
The Long-term Implications and Future Developments
As we delve into the future, we can expect the importance of data governance to amplify even further. As organizations will increasingly use AI and machine learning models, there will be a growing expectation for reliable results and consistent performance from these models. This is where effective data governance will shine.
- Improved AI Efficacy: Proper data governance will tackle the pressing issues limiting AI efficacy by ensuring clean, high-quality, and relevant data feed into these models.
- Increased Performance Consistency: It will ensure consistent performance by minimizing discrepancies in data collection and processing.
- Reduced AI Fatigue: With proper application, it can significantly reduce instances of AI fatigue by reinforcing efficient data management practices.
However, the implementation of proper data governance will raise concerns about data privacy and security. As organizations collect and process more data, they need to address these issues vigilantly. Implementing secure, privacy-preserving practices should be a vital part of any data governance strategy.
Actionable Advice for the Future
Based on these insights, the role of data governance in AI applications is undoubtedly enormous. It is ultimately crucial for organizations to have a strong data governance policy at the base of their AI operations.
- Implement Solid Data Governance Policies: All projects dealing with AI models should have solid data governance policies. These policies should focus on maintaining data integrity, quality, authenticity, and security.
- Invest in Data Quality: Organizations should invest heavily in ensuring data quality. This can be done through continuous data profiling, cleaning, validation, and enrichment.
- Emphasize on Data Security: As companies collect more data, there should be increased emphasis on preserving data security and privacy.
In conclusion, a strong data governance framework may very well hold the key to overcoming AI fatigue and building more robust AI models in the future.
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by jsendak | Dec 30, 2023 | Computer Science
Protecting Privacy in Federated Recommender Systems: Introducing UC-FedRec
Federated recommender (FedRec) systems have been developed to address privacy concerns in recommender systems by allowing users to train a shared recommendation model on their local devices, thereby preventing raw data transmissions and collections. However, a common FedRec approach may still leave users vulnerable to attribute inference attacks, where personal attributes can be easily inferred from the learned model.
Moreover, traditional FedRecs often fail to consider the diverse privacy preferences of users, resulting in difficulties in balancing recommendation utility and privacy preservation. This can lead to unnecessary recommendation performance loss or private information leakage.
In order to address these issues, we propose a novel user-consented federated recommendation system (UC-FedRec) that allows users to define their own privacy preferences while still enjoying personalized recommendations. By paying a minimum recommendation accuracy price, UC-FedRec offers flexibility in meeting various privacy demands. Users can have control over their data and make informed decisions about the level of privacy they are comfortable with.
Our experiments on real-world datasets demonstrate that UC-FedRec outperforms baseline approaches in terms of efficiency and flexibility. With UC-FedRec, users can have peace of mind knowing that their privacy is protected without sacrificing the quality of personalized recommendations.
Abstract:Recommender systems can be privacy-sensitive. To protect users’ private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared recommendation model on local devices and prevent raw data transmissions and collections. However, the recommendation model learned by a common FedRec may still be vulnerable to private information leakage risks, particularly attribute inference attacks, which means that the attacker can easily infer users’ personal attributes from the learned model. Additionally, traditional FedRecs seldom consider the diverse privacy preference of users, leading to difficulties in balancing the recommendation utility and privacy preservation. Consequently, FedRecs may suffer from unnecessary recommendation performance loss due to over-protection and private information leakage simultaneously. In this work, we propose a novel user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users by paying a minimum recommendation accuracy price. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent. Experiments conducted on different real-world datasets demonstrate that our framework is more efficient and flexible compared to baselines.
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by jsendak | Dec 29, 2023 | Computer Science
A Method for Estimating Users’ Preferences for Websites
A site’s recommendation system relies on understanding its users’ preferences in order to offer relevant recommendations. These preferences are based on the attributes that make up the items and content shown on the site, and they are estimated from the data of users’ interactions with the site. However, there is another important aspect of users’ preferences that is often overlooked – their preferences for the site itself over other sites. This shows the users’ base level propensities to engage with the site.
Estimating these preferences for the site faces significant obstacles. Firstly, the focal site usually has no data on its users’ interactions with other sites, making these interactions their unobserved behaviors for the focal site. Secondly, the Machine Learning literature in recommendation does not provide a model for this particular situation. Even if a model is developed, the problem of lacking ground truth evaluation data still remains.
In this article, we present a method to estimate individual users’ preferences for a focal site using only the data from that site. By computing the focal site’s share of a user’s online engagements, we can personalize recommendations to individual users. We introduce a Hierarchical Bayes Method and demonstrate two different ways of estimation – Markov Chain Monte Carlo and Stochastic Gradient with Langevin Dynamics.
We also propose an evaluation framework for the model using only the focal site’s data. This allows the site to test the model and assess its effectiveness. Our results show strong support for this approach to computing personalized share of engagement and its evaluation.
Abstract:A site’s recommendation system relies on knowledge of its users’ preferences to offer relevant recommendations to them. These preferences are for attributes that comprise items and content shown on the site, and are estimated from the data of users’ interactions with the site. Another form of users’ preferences is material too, namely, users’ preferences for the site over other sites, since that shows users’ base level propensities to engage with the site. Estimating users’ preferences for the site, however, faces major obstacles because (a) the focal site usually has no data of its users’ interactions with other sites; these interactions are users’ unobserved behaviors for the focal site; and (b) the Machine Learning literature in recommendation does not offer a model of this situation. Even if (b) is resolved, the problem in (a) persists since without access to data of its users’ interactions with other sites, there is no ground truth for evaluation. Moreover, it is most useful when (c) users’ preferences for the site can be estimated at the individual level, since the site can then personalize recommendations to individual users. We offer a method to estimate individual user’s preference for a focal site, under this premise. In particular, we compute the focal site’s share of a user’s online engagements without any data from other sites. We show an evaluation framework for the model using only the focal site’s data, allowing the site to test the model. We rely upon a Hierarchical Bayes Method and perform estimation in two different ways – Markov Chain Monte Carlo and Stochastic Gradient with Langevin Dynamics. Our results find good support for the approach to computing personalized share of engagement and for its evaluation.
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