Multi-Domain Active Learning for Recommendation
Authors: Zihan Zhang, Xiaoming Jin, Lianghao Li, Guiguang Ding, Qiang Yang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our strategy with five state-of-the-art baselines on five different multi-domain recommendation tasks, which are constituted by three real-world data sets. The experimental results show that our strategy performs significantly better than all the baselines and reduces human labeling efforts by at least 5.6%, 8.3%, 11.8%, 12.5% and 15.4% on the five tasks, respectively. |
| Researcher Affiliation | Academia | Tsinghua National Laboratory for Information Science and Technology (TNList) School of Software, Tsinghua University, Beijing, China Hong Kong University of Science and Technology, Hong Kong |
| Pseudocode | Yes | Algorithm 1: Multi-Domain Active Learning |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | The paper mentions several real-world domains (Douban Book, Movie Lens, Each Movie, Netflix) and provides general URLs in footnotes (e.g., 'http://www.douban.com', 'http://www.grouplens.org/datasets/movielens/'). However, these are not direct download links to the specific datasets or versions used, nor are formal citations provided for dataset access. The paper states, 'We first preprocess the data sets in a way similar to (Li, Yang, and Xue 2009b)' implying prior acquisition and processing. |
| Dataset Splits | No | In the experiments, we randomly divided the whole rating data set into three parts: training set (10%), test set (20%) and rating pool (70%). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For the Multi AL, both μ and ω in Equation 5 are set to 1 for the assumption that the specific knowledge and independent knowledge are equally important. In each active iteration, we query 400 D unknown ratings of the user-item pairs from P and then add the rating of each user-item pair into the training set, where D is the number of domains in the task. After that, the multi-domain recommendation model is re-trained on the new training set. In total, we do 50 active iterations, which totally queries 20000 D ratings from P. |