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..
Basket-Sensitive Personalized Item Recommendation
Authors: Duc-Trong Le, Hady W. Lauw, Yuan Fang
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-life datasets from different domains empirically validate these models against baselines based on matrix factorization and association rules. Through experiments on three real-life datasets from different domains, we conduct an empirical analysis of BFM and CBFM in Section 5, which also includes a comparison to an association rule-based baseline. |
| Researcher Affiliation | Collaboration | School of Information Systems, Singapore Management University, Singapore Institute for Infocomm Research, A*STAR, Singapore |
| Pseudocode | No | The paper describes models and equations but does not provide a separate pseudocode block or algorithm. |
| Open Source Code | No | The paper mentions implementing BFM in Java based on lib FM (footnote 5 links to http://www.libfm.org), but this refers to a third-party tool they used, not their own source code release. |
| Open Datasets | Yes | Ta Feng2: This is a retail market dataset...2http://recsyswiki.com/wiki/Grocery_shopping_datasets; Bei Ren3: This comes from a large retailer in China...3http://www.brjt.cn; Foursquare4: This consists of users check-ins at various points of interest in Singapore [Yuan et al., 2013].4http://www.ntu.edu.sg/home/gaocong/datacode.htm |
| Dataset Splits | Yes | For each user, we sort her transactions chronologically. The last transaction will be part of the testing set. The second-last transaction will be part of the validation set. The rest will be part of the training set. |
| Hardware Specification | Yes | Timing is based on a PC with Intel Core i5 3.2GHz with 8GB RAM. |
| Software Dependencies | No | The paper mentions 'Java' and 'lib FM' but does not provide specific version numbers for either, nor for any other software libraries or dependencies used. |
| Experiment Setup | Yes | For these experiments, we use latent factor dimension K = 8 and regularization parameter λθ = 0.01, which are also the defaults of lib FM. The initial learning rate η is 0.0001 for Ta Feng, Bei Ren and 0.001 Foursquare respectively to reflect their relative sparsity. We further apply the Bold-Driver adaptive learning rate [Battiti, 1989]. |