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..
Exploring the Context of Locations for Personalized Location Recommendations
Authors: Xin Liu, Yong Liu, Xiaoli Li
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments over four real datasets. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art location recommendation methods. |
| Researcher Affiliation | Academia | Xin Liu, Yong Liu and Xiaoli Li Institute for Infocomm Research (I2R), A*STAR, Singapore EMAIL |
| Pseudocode | No | The paper describes algorithmic steps and equations in paragraph form, but does not contain a clearly labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | No | The paper mentions "word2vec1" with a footnote linking to "https://code.google.com/p/word2vec/", but this refers to a third-party tool used, not the authors' own implementation code for the described methodology. |
| Open Datasets | Yes | The evaluation is conducted over real-world location-based social network data [Liu et al., 2014] collected from Gowalla 2. The data contains users check-in information, including geographical coordinates, time stamps, etc. generated before June 1, 2011 in 4 US cities: Austin, Los Angeles, Chicago, and Houston. Table 1 summarizes the statistics of the data, where Nu, Nl, and Nc denote the number of users, locations, and check-ins respectively. Moreover, the category information of each observed location has also been collected. Locations in Gowalla are classified into 7 main categories: community, entertainment, food, nightlife, outdoors, shopping, and travel. 2http://www.yongliu.org/datasets. |
| Dataset Splits | No | The paper mentions a train/test split: "For each method, we use the check-in data before March 28th, 2011 (around 80% of all check-ins) to train the models, and the rest data is used for testing." However, it does not specify a separate validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of 'word2vec' but does not specify any version numbers for this or any other software libraries or dependencies used in the implementation. |
| Experiment Setup | Yes | For WRMF, we set the latent factor vector dimensionality, , and regularization parameter to 150, 10, and 0.01 respectively; for PTMF, category level 2 is considered, latent factor vector dimensionality, learning rate, and regularization parameters are set to 5, 0.0001, and 0.01 respectively. For SG-CWARP, the latent factor vector dimensionality, , regularization parameters are set to 200, 1, and 0.01 respectively; we also set optimal context window size for different datasets. For Temp MF, we set the latent factor vector dimensionality, learning rate, user-preference parameter, location-characteristic parameter, and the time regularization parameter to 10, 0.0001, 2, 2, and 1 respectively. |