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..
Synthesizing Aspect-Driven Recommendation Explanations from Reviews
Authors: Trung-Hoang Le, Hady W. Lauw
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on datasets of several product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text generation. |
| Researcher Affiliation | Academia | Trung-Hoang Le and Hady W. Lauw Singapore Management University, Singapore EMAIL |
| Pseudocode | Yes | Algorithm 1 SEER-Greedy; Algorithm 2 Opinion Substitution |
| Open Source Code | No | The paper links to third-party code implementations for EFM and MTER, but does not provide a link or explicit statement for the availability of their own SEER framework's source code. |
| Open Datasets | Yes | Experiments use four public datasets of Amazon reviews1 [Mc Auley et al., 2015] of varying categories: Computer and Accessories (Computer), Camera and Photo (Camera), Toys and Games (Toy), Cell Phones and Accessories (Cellphone). Preprocessing follows [Wang et al., 2018a]. For each category, we retrieve the most common aspects covering 90% of opinion phrases and filter out users and items with fewer than five reviews. The remaining are split into training, validation, and test at a ratio of 0.6 : 0.2 : 0.2 for every user chronologically. Sentences in validation and test with opinions or aspects that had not appeared in training were excluded. Table 2 shows some basic statistics of the datasets. 1http://jmcauley.ucsd.edu/data/amazon/ |
| Dataset Splits | Yes | The remaining are split into training, validation, and test at a ratio of 0.6 : 0.2 : 0.2 for every user chronologically. |
| Hardware Specification | Yes | Experiments were run on machine with Intel Xeon E5-2650v4 2.20 GHz CPU and 256GB RAM. |
| Software Dependencies | No | The paper mentions the use of "CPLEX4 solver" but does not provide a specific version number. It does not list other key software components with version numbers required for replication. |
| Experiment Setup | Yes | For EFM2, as in the original work, the latent factor and explicit factor dimensions are 60 and 40. For MTER, we adopt the default setting of the author s implementation3. ... For ASC2V, we train with similar setting as C2V, using RMSprop for optimization. |