Temporal Collaborative Topic Regression (T-CTR)

Implementation for the paper Time-aware Collaborative Topic Regression: Towards Higher Relevance in Textual Item Recommendation.
Extends Collaborative Topic Modelling CTR Recommender System used in the work of Wang and Blei to consider the temporal aspect in recommendation.
This project generates a recommender system model. To evaluate the generated model, use the Recommender Evaluator System, which is responsible for splitting the labeled data and computing the evaluation metrics.

Rquirements:

python 3.5
numpy

Dataset:

This work uses dataset collected from citeulike, you can downlowd it from here

Please cite using the following BibTex entry:

@inproceedings{DBLP:conf/sigir/Alzogbi18,
  author    = {Anas Alzogbi},
  title     = {Time-aware Collaborative Topic Regression: Towards Higher Relevance
               in Textual Item Recommendation},
  booktitle = {Proceedings of the 3rd Joint Workshop on Bibliometric-enhanced Information
               Retrieval and Natural Language Processing for Digital Libraries {(BIRNDL}
               2018) co-located with the 41st International {ACM} {SIGIR} Conference
               on Research and Development in Information Retrieval {(SIGIR} 2018),
               Ann Arbor, USA, July 12, 2018.},
  pages     = {10--23},
  year      = {2018},
  crossref  = {DBLP:conf/sigir/2018birndl},
  url       = {http://ceur-ws.org/Vol-2132/paper2.pdf},
  timestamp = {Mon, 09 Jul 2018 18:23:12 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/sigir/Alzogbi18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Directory structure:

anasalzogbi/T-CTR

Temporal Collaborative Topic Regression (T-CTR)

Rquirements:

Dataset:

Please cite using the following BibTex entry:

Directory structure: