The blog post "Don't use cosine similarity carelessly" cautions against the naive application of cosine similarity, particularly in machine learning and recommendation systems, without a thorough understanding of its implications and potential pitfalls. The author meticulously illustrates how cosine similarity, while effective in certain scenarios, can produce misleading or undesirable results when the underlying data possesses specific characteristics.
The core argument revolves around the fact that cosine similarity solely focuses on the angle between vectors, effectively disregarding the magnitude or scale of those vectors. This can be problematic when comparing items with drastically different scales of interaction or activity. For instance, in a movie recommendation system, a user who consistently rates movies highly will appear similar to another user who rates movies highly, even if their taste in genres is vastly different. This is because the large magnitude of their ratings dominates the cosine similarity calculation, obscuring the nuanced differences in their preferences. The author underscores this with an example of book recommendations, where a voracious reader may appear similar to other avid readers regardless of their preferred genres simply due to the high volume of their reading activity.
The author further elaborates this point by demonstrating how cosine similarity can be sensitive to "bursts" of activity. A sudden surge in interaction with certain items, perhaps due to a promotional campaign or temporary trend, can disproportionately influence the similarity calculations, potentially leading to recommendations that are not truly reflective of long-term preferences.
The post provides a concrete example using a movie rating dataset. It showcases how users with different underlying preferences can appear deceptively similar based on cosine similarity if one user has rated many more movies overall. The author emphasizes that this issue becomes particularly pronounced in sparsely populated datasets, common in real-world recommendation systems.
The post concludes by suggesting alternative approaches that consider both the direction and magnitude of the vectors, such as Euclidean distance or Manhattan distance. These metrics, unlike cosine similarity, are sensitive to differences in scale and are therefore less susceptible to the pitfalls described earlier. The author also encourages practitioners to critically evaluate the characteristics of their data before blindly applying cosine similarity and to consider alternative metrics when magnitude plays a crucial role in determining true similarity. The overall message is that while cosine similarity is a valuable tool, its limitations must be recognized and accounted for to ensure accurate and meaningful results.
This Distill publication provides a comprehensive yet accessible introduction to Graph Neural Networks (GNNs), meticulously explaining their underlying principles, mechanisms, and potential applications. The article begins by establishing the significance of graphs as a powerful data structure capable of representing complex relationships between entities, ranging from social networks and molecular structures to knowledge bases and recommendation systems. It underscores the limitations of traditional deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which struggle to effectively process the irregular and non-sequential nature of graph data.
The core concept of GNNs, as elucidated in the article, revolves around the aggregation of information from neighboring nodes to generate meaningful representations for each node within the graph. This process is achieved through iterative message passing, where nodes exchange information with their immediate neighbors and update their own representations based on the aggregated information received. The article meticulously breaks down this message passing process, detailing how node features are transformed and combined using learnable parameters, effectively capturing the structural dependencies within the graph.
Different types of GNN architectures are explored, including Graph Convolutional Networks (GCNs), GraphSAGE, and GATs (Graph Attention Networks). GCNs utilize a localized convolution operation to aggregate information from neighboring nodes, while GraphSAGE introduces a sampling strategy to improve scalability for large graphs. GATs incorporate an attention mechanism, allowing the network to assign different weights to neighboring nodes based on their relevance, thereby capturing more nuanced relationships within the graph.
The article provides clear visualizations and interactive demonstrations to facilitate understanding of the complex mathematical operations involved in GNNs. It also delves into the practical aspects of implementing GNNs, including how to represent graph data, choose appropriate aggregation functions, and select suitable loss functions for various downstream tasks.
Furthermore, the article discusses different types of graph tasks that GNNs can effectively address. These include node-level tasks, such as node classification, where the goal is to predict the label of each individual node; edge-level tasks, such as link prediction, where the objective is to predict the existence or absence of edges between nodes; and graph-level tasks, such as graph classification, where the aim is to categorize entire graphs based on their structure and node features. Specific examples are provided for each task, illustrating the versatility and applicability of GNNs in diverse domains.
Finally, the article concludes by highlighting the ongoing research and future directions in the field of GNNs, touching upon topics such as scalability, explainability, and the development of more expressive and powerful GNN architectures. It emphasizes the growing importance of GNNs as a crucial tool for tackling complex real-world problems involving relational data and underscores the vast potential of this rapidly evolving field.
The Hacker News post titled "A Gentle Introduction to Graph Neural Networks" linking to a Distill.pub article has generated several comments discussing various aspects of Graph Neural Networks (GNNs).
Several commenters praise the Distill article for its clarity and accessibility. One user appreciates its gentle introduction, highlighting how it effectively explains the core concepts without overwhelming the reader with complex mathematics. Another commenter specifically mentions the helpful visualizations, stating that they significantly aid in understanding the mechanisms of GNNs. The interactive nature of the article is also lauded, with users pointing out how the ability to manipulate and experiment with the visualizations enhances comprehension and provides a deeper, more intuitive grasp of the subject matter.
The discussion also delves into the practical applications and limitations of GNNs. One commenter mentions their use in drug discovery and material science, emphasizing the potential of GNNs to revolutionize these fields. Another user raises concerns about the computational cost of training large GNNs, particularly with complex graph structures, acknowledging the challenges in scaling these models for real-world applications. This concern sparks further discussion about potential optimization strategies and the need for more efficient algorithms.
Some comments focus on specific aspects of the GNN architecture and training process. One commenter questions the effectiveness of message passing in certain scenarios, prompting a discussion about alternative approaches and the limitations of the message-passing paradigm. Another user inquires about the choice of activation functions and their impact on the performance of GNNs. This leads to a brief exchange about the trade-offs between different activation functions and the importance of selecting the appropriate function based on the specific task.
Finally, a few comments touch upon the broader context of GNNs within the field of machine learning. One user notes the growing popularity of GNNs and their potential to address complex problems involving relational data. Another commenter draws parallels between GNNs and other deep learning architectures, highlighting the similarities and differences in their underlying principles. This broader perspective helps to situate GNNs within the larger landscape of machine learning and provides context for their development and future directions.
Eli Bendersky's blog post, "ML in Go with a Python Sidecar," explores a practical approach to integrating machine learning (ML) models, typically developed and trained in Python, into applications written in Go. Bendersky acknowledges the strengths of Go for building robust and performant backend systems while simultaneously recognizing Python's dominance in the ML ecosystem, particularly with libraries like TensorFlow, PyTorch, and scikit-learn. Instead of attempting to replicate the extensive ML capabilities of Python within Go, which could prove complex and less efficient, he advocates for a "sidecar" architecture.
This architecture involves running a separate Python process alongside the main Go application. The Go application interacts with the Python ML service through inter-process communication (IPC), specifically using gRPC. This allows the Go application to leverage the strengths of both languages: Go handles the core application logic, networking, and other backend tasks, while Python focuses solely on executing the ML model.
Bendersky meticulously details the implementation of this sidecar pattern. He provides comprehensive code examples demonstrating how to define the gRPC service in Protocol Buffers, implement the Python server utilizing TensorFlow to load and execute a pre-trained model, and create the corresponding Go client to communicate with the Python server. The example focuses on a simple image classification task, where the Go application sends an image to the Python sidecar, which then returns the predicted classification label.
The post highlights several advantages of this approach. Firstly, it enables clear separation of concerns. The Go and Python components remain independent, simplifying development, testing, and deployment. Secondly, it allows leveraging existing Python ML code and expertise without requiring extensive Go ML libraries. Thirdly, it provides flexibility for scaling the ML component independently from the main application. For example, the Python sidecar could be deployed on separate hardware optimized for ML tasks.
Bendersky also discusses the performance implications of this architecture, acknowledging the overhead introduced by IPC. He mentions potential optimizations, like batching requests to the Python sidecar to minimize communication overhead. He also suggests exploring alternative IPC mechanisms besides gRPC if performance becomes a critical bottleneck.
In summary, the blog post presents a pragmatic solution for incorporating ML models into Go applications by leveraging a Python sidecar. The provided code examples and detailed explanations offer a valuable starting point for developers seeking to implement a similar architecture in their own projects. While acknowledging the inherent performance trade-offs of IPC, the post emphasizes the significant benefits of this approach in terms of development simplicity, flexibility, and the ability to leverage the strengths of both Go and Python.
The Hacker News post titled "ML in Go with a Python Sidecar" (https://news.ycombinator.com/item?id=42108933) elicited a modest number of comments, generally focusing on the practicality and trade-offs of the proposed approach of using Python for machine learning tasks within a Go application.
One commenter highlighted the potential benefits of this approach, especially for computationally intensive ML tasks where Go's performance might be a bottleneck. They acknowledged the convenience and rich ecosystem of Python's ML libraries, suggesting that leveraging them while keeping the core application logic in Go could be a sensible compromise. This allows for utilizing the strengths of both languages: Go for its performance and concurrency in handling application logic, and Python for its mature ML ecosystem.
Another commenter questioned the performance implications of the inter-process communication between Go and the Python sidecar, particularly for real-time applications. They raised concerns about the overhead introduced by serialization and deserialization of data being passed between the two processes. This raises the question of whether the benefits of using Python for ML outweigh the performance cost of this communication overhead.
One comment suggested exploring alternatives like using shared memory for communication between Go and Python, as a potential way to mitigate the performance overhead mentioned earlier. This alternative approach aims to optimize the data exchange by avoiding the serialization/deserialization steps, leading to potentially faster processing.
A further comment expanded on the shared memory idea, specifically mentioning Apache Arrow as a suitable technology for this purpose. They argued that Apache Arrow’s columnar data format could further enhance the performance and efficiency of data exchange between the Go and Python processes, specifically highlighting zero-copy reads for improved efficiency.
The discussion also touched upon the complexity introduced by managing two separate processes and the potential challenges in debugging and deployment. One commenter briefly discussed potential deployment complexities with two processes and debugging. This contributes to a more holistic view of the proposed architecture, considering not only its performance characteristics but also the operational aspects.
Another commenter pointed out the maturity and performance improvements in Go's own machine learning libraries, suggesting they might be a viable alternative in some cases, obviating the need for a Python sidecar altogether. This introduces the consideration of whether the proposed approach is necessary in all scenarios, or if native Go libraries are sufficient for certain ML tasks.
Finally, one commenter shared an anecdotal experience, confirming the practicality of the Python sidecar approach. They mentioned successfully using a similar setup in production, lending credibility to the article's proposal. This real-world example provides some validation for the discussed approach and suggests it's not just a theoretical concept but a practical solution.
Summary of Comments ( 70 )
https://news.ycombinator.com/item?id=42704078
Hacker News users generally agreed with the article's premise, cautioning against blindly applying cosine similarity. Several commenters pointed out that the effectiveness of cosine similarity depends heavily on the specific use case and data distribution. Some highlighted the importance of normalization and feature scaling, noting that cosine similarity is sensitive to these factors. Others offered alternative methods, such as Euclidean distance or Manhattan distance, suggesting they might be more appropriate in certain situations. One compelling comment underscored the importance of understanding the underlying data and problem before choosing a similarity metric, emphasizing that no single metric is universally superior. Another emphasized how important preprocessing is, highlighting TF-IDF and BM25 as helpful techniques for text analysis before using cosine similarity. A few users provided concrete examples where cosine similarity produced misleading results, further reinforcing the author's warning.
The Hacker News post "Don't use cosine similarity carelessly" (https://news.ycombinator.com/item?id=42704078) sparked a discussion with several insightful comments regarding the article's points about the pitfalls of cosine similarity.
Several commenters agreed with the author's premise, emphasizing the importance of understanding the implications of using cosine similarity. One commenter highlighted the issue of scale invariance, pointing out that two vectors can have a high cosine similarity even if their magnitudes are vastly different, which can be problematic in certain applications. They used the example of comparing customer purchase behavior where one customer buys small quantities frequently and another buys large quantities infrequently. Cosine similarity might suggest they're similar, ignoring the significant difference in total spending.
Another commenter pointed out that the article's focus on document comparison and TF-IDF overlooks common scenarios like comparing embeddings from large language models (LLMs). They argue that in these cases, magnitude does often carry significant semantic meaning, and normalization can be detrimental. They specifically mentioned the example of sentence embeddings, where longer sentences tend to have larger magnitudes and often carry more information. Normalizing these embeddings would lose this information. This commenter suggested that the article's advice is too general and doesn't account for the nuances of various applications.
Expanding on this, another user added that even within TF-IDF, the magnitude can be a meaningful signal, suggesting that document length could be a relevant factor for certain types of comparisons. They suggested that blindly applying cosine similarity without considering such factors can be problematic.
One commenter offered a concise summary of the issue, stating that cosine similarity measures the angle between vectors, discarding information about their magnitudes. They emphasized the need to consider whether magnitude is important in the specific context.
Finally, a commenter shared a personal anecdote about a machine learning competition where using cosine similarity instead of Euclidean distance drastically improved their results. They attributed this to the inherent sparsity of the data, highlighting that the appropriateness of a similarity metric heavily depends on the nature of the data.
In essence, the comments generally support the article's caution against blindly using cosine similarity. They emphasize the importance of considering the specific context, understanding the implications of scale invariance, and recognizing that magnitude can often carry significant meaning depending on the application and data.