Basket-Sensitive Personalized Item Recommendation
Authors: Duc-Trong Le, Hady W. Lauw, Yuan Fang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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]. |