Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Improving Entity Recommendation with Search Log and Multi-Task Learning
Authors: Jizhou Huang, Wei Zhang, Yaming Sun, Haifeng Wang, Ting Liu
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach using large-scale, real-world search logs of a widely used commercial Web search engine. The experimental results show that incorporating context information significantly improves entity recommendation, and learning the model in a multi-task learning setting could bring further improvements. |
| Researcher Affiliation | Collaboration | Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China Baidu Inc., Beijing, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Training the multi-task DNN model |
| Open Source Code | No | The paper does not include an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The data sets are collected from a commercial Web search engine and are described as 'large-scale, real-world data sets'. There is no indication or link provided for public access to these datasets. |
| Dataset Splits | Yes | Tr was randomly split into training set T l r (80%), validation set T v r (10%), and test set T t r (10%). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using Bidirectional LSTM and Gradient Boosted Decision Tree (GBDT) but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We use 2-layer Bi LSTM with 128 hidden units. The dimensions of word embeddings, query embeddings, document embeddings, and entity embeddings are set to 256. The mini-batch size is set to 512. The learning rate is initially set to 0.1, which is decayed by a factor of 0.9 after every 10 epochs. |