A Greedy Approach for Budgeted Maximum Inner Product Search
Authors: Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform extensive empirical comparisons to compare Greedy-MIPS with other state-of-the-art fast MIPS approaches on both real-world and synthetic datasets: |
| Researcher Affiliation | Collaboration | Hsiang-Fu Yu Amazon Inc. rofuyu@cs.utexas.edu Cho-Jui Hsieh University of California, Davis chohsieh@ucdavis.edu Qi Lei The University of Texas at Austin leiqi@ices.utexas.edu Inderjit S. Dhillon The University of Texas at Austin inderjit@cs.utexas.edu |
| Pseudocode | Yes | Algorithm 1 Cond Iter: an iterator over j 2 {1, . . . , n} based on the conditional ranking t(j|w). This code assumes that the k sorted index arrays st[r], r=1, . . . , n, t=1, . . . , k are available. class Cond Iter: def constructor(dim_idx, query_val): t, w, ptr dim_idx, query_val, 1 def current(): st[ptr] if w > 0, st[n ptr + 1] otherwise. def has Next(): return (ptr < n) def get Next(): ptr ptr + 1 and return current() |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use netflix and yahoo-music as our real-world recommender system datasets. There are 17, 770 and 624, 961 items in netflix and yahoo-music, respectively. In particular, we obtain the user embeddings {wi} 2 Rk and item embeddings hj 2 Rk by the standard low-rank matrix factorization [4] with k 2 {50, 200}. |
| Dataset Splits | No | The paper mentions using real-world and synthetic datasets and evaluating on 'a randomly selected 2,000 query vectors' but does not provide specific training, validation, or test dataset split percentages or sample counts. |
| Hardware Specification | No | The paper mentions that 'all the compared approaches are implemented in C++' and discusses runtime, but it does not specify any hardware details such as CPU/GPU models or memory used for the experiments. |
| Software Dependencies | No | The paper states that 'all the compared approaches are implemented in C++', but it does not specify any ancillary software dependencies with version numbers (e.g., specific libraries, frameworks, or compilers). |
| Experiment Setup | No | The paper mentions varying parameters like 'depth of PCA tree' for PCA-MIPS, 'values (a, b)' for LSH-MIPS, and 'number of samples' for Diamond-MSIPS to control trade-offs, but it does not provide specific hyperparameter values or detailed system-level training configurations (e.g., learning rates, batch sizes, optimizers) for its own method or the baselines. |