New Loss Functions for Fast Maximum Inner Product Search
Quantization based methods are popular for solving large scale maximum inner product search problems. However, in most traditional quantization works, the objective is to minimize the reconstruction error for datapoints to be searched. In this work, we focus directly on minimizing error in inner product approximation and derive a new class of quantization loss functions. One key aspect of the new loss functions is that we weight the error term based on the value of the inner product, giving more importance to pairs of queries and datapoints whose inner products are high. We provide theoretical grounding to the new quantization loss function, which is simple, intuitive and able to work with a variety of quantization techniques, including binary quantization and product quantization. We conduct experiments on public benchmarking datasets url{http://ann-benchmarks.com} to demonstrate that our method using the new objective outperforms other state-of-the-art methods. We are committed to release our source code.
Summary: The authors propose a new loss function for solving large-scale inner product search that rely on quantization, based on the intuition that all pairs of (query, database vector) are not equally important for a given query. In particular, the authors weight the reconstruction error so that the pairs with a higher scalar product are more precisely quantized (as they lie among the most plausible candidates). Strengths of the paper: - The paper is well written and easy to follow. In particular, the intuition of the method is well explained in Figure 1 and the setup in Section 3 is well formulated. - The proposed method works with a variety of quantization approaches, such as binary, simple PQ or LSQ (even if the authors aren*t able to report results for this last method due to technical issues as explained in Appendix 7.7) Weaknesses of the paper: - The related work could be more detailed, see for example: *Spreading vectors for Similarity Search*, Sablayrolles et al. ; *Pairwise Quantization*, Babenko et al ; *Unsupervised Neural Quantization for Compressed-Domain Similarity Search*, Morozov et al. Justification of rating: The paper proposes a new loss function that weights the scalar products differently according to their importance than can be applied to a wide range of existing quantization methods. However, the strength of experimental results (in particular the fact that LSQ or other cited references above are missing) remains unclear.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
New Loss Functions for Fast Maximum Inner Product Search
Quantization based methods are popular for solving large scale maximum inner product search problems. However, in most traditional quantization works, the objective is to minimize the reconstruction error for datapoints to be searched. In this work, we focus directly on minimizing error in inner product approximation and derive a new class of quantization loss functions. One key aspect of the new loss functions is that we weight the error term based on the value of the inner product, giving more importance to pairs of queries and datapoints whose inner products are high. We provide theoretical grounding to the new quantization loss function, which is simple, intuitive and able to work with a variety of quantization techniques, including binary quantization and product quantization. We conduct experiments on public benchmarking datasets url{http://ann-benchmarks.com} to demonstrate that our method using the new objective outperforms other state-of-the-art methods. We are committed to release our source code.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



Summary: The authors propose a new loss function for solving large-scale inner product search that rely on quantization, based on the intuition that all pairs of (query, database vector) are not equally important for a given query. In particular, the authors weight the reconstruction error so that the pairs with a higher scalar product are more precisely quantized (as they lie among the most plausible candidates). Strengths of the paper: - The paper is well written and easy to follow. In particular, the intuition of the method is well explained in Figure 1 and the setup in Section 3 is well formulated. - The proposed method works with a variety of quantization approaches, such as binary, simple PQ or LSQ (even if the authors aren*t able to report results for this last method due to technical issues as explained in Appendix 7.7) Weaknesses of the paper: - The related work could be more detailed, see for example: *Spreading vectors for Similarity Search*, Sablayrolles et al. ; *Pairwise Quantization*, Babenko et al ; *Unsupervised Neural Quantization for Compressed-Domain Similarity Search*, Morozov et al. Justification of rating: The paper proposes a new loss function that weights the scalar products differently according to their importance than can be applied to a wide range of existing quantization methods. However, the strength of experimental results (in particular the fact that LSQ or other cited references above are missing) remains unclear.
assistant
Summary: The authors propose a new loss function for solving large-scale inner product search that rely on quantization, based on the intuition that all pairs of (query, database vector) are not equally important for a given query. In particular, the authors weight the reconstruction error so that the pairs with a higher scalar product are more precisely quantized (as they lie among the most plausible candidates). Strengths of the paper: - The paper is well written and easy to follow. In particular, the intuition of the method is well explained in Figure 1 and the setup in Section 3 is well formulated. - The proposed method works with a variety of quantization approaches, such as binary, simple PQ or LSQ (even if the authors aren*t able to report results for this last method due to technical issues as explained in Appendix 7.7) Weaknesses of the paper: - The related work could be more detailed, see for example: *Spreading vectors for Similarity Search*, Sablayrolles et al. ; *Pairwise Quantization*, Babenko et al ; *Unsupervised Neural Quantization for Compressed-Domain Similarity Search*, Morozov et al. Justification of rating: The paper proposes a new loss function that weights the scalar products differently according to their importance than can be applied to a wide range of existing quantization methods. However, the strength of experimental results (in particular the fact that LSQ or other cited references above are missing) remains unclear.