Are Features Equally Representative? A Feature-Centric Recommendation
Authors: Chenyi Zhang, Ke Wang, Ee-peng Lim, Qinneng Xu, Jianling Sun, Hongkun Yu
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Evaluation We report our findings on the evaluation of the proposed feature-centric recommendation against well known baselines using real life data sets. |
| Researcher Affiliation | Academia | Chenyi Zhang1,2, Ke Wang2 , Ee-peng Lim3, Qinneng Xu4, Jianling Sun1 and Hongkun Yu5 1College of Computer Science, Zhejiang University 2School of Computing Science, Simon Fraser University 3School of Information Systems, Singapore Management University 4Department of Systems Engineering and Engineering Management, City University of Hong kong 5Department of Computer Science, University of Illinois at Urbana-Champaign |
| Pseudocode | No | The paper describes methods through text and mathematical equations but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (specific link, explicit statement, or supplementary material reference) for open-source code related to the described methodology. |
| Open Datasets | Yes | We employed four data sets: Delicious, Lastfm, DBLP, and Movielens. The first two data sets were recommended as benchmark data sets for studying recommender systems by the 2011 Het Rec conference1. These data sets contain user s tagging information on bookmarks and music songs, which expresses user s ratings or preferences on items. We treat tags as the features of an item. Delicious contains 1867 users ratings on 69223 items with 40897 unique features. Lastfm contains 2100 users ratings on 18744 items with 12647 unique features. The third data set DBLP contains authors, papers and citation information from an academic network. We treated authors as users, papers as items, each publishing/citation of a paper as user s rating on the paper, and treated the venues and authors of a paper as the features of the paper. After removing the users with fewer than 10 papers from the original DBLP data set2, the final data set contains 6815 users ratings on 78475 items with 81858 unique features. All the above data sets have binary ratings. The fourth data set Movielens, also recommended by the 2011 Het Rec conference, was collected from a movie review system. This data set has the ratings ranged from 1 to 5. We removed those movies without any ratings. The resulting data set has 1857 users ratings on 4721 items with 8288 unique features (i.e., tags). The statistics of these data sets are found in Table 1. |
| Dataset Splits | Yes | We conducted 10-fold cross validation for all data sets. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions running code from cited works (Rendle 2012, Agarwal and Chen 2009) but does not provide specific version numbers for these or any other software dependencies, such as programming languages or libraries, used for its own implementation. |
| Experiment Setup | Yes | For all methods except for SIM, we adopt the dimensionality of D = 20 for latent vectors and the learning rate of η = 0.0001. |