• Anwar Z, Afzal H and Iltaf N. (2024). Learning beyond books: A hybrid model to learn real‐world problems. Computer Applications in Engineering Education. 10.1002/cae.22792. 32:6. Online publication date: 1-Nov-2024.

    https://onlinelibrary.wiley.com/doi/10.1002/cae.22792

  • Kim K, Ghatpande S, Kim D, Zhou X, Liu K, Bissyandé T, Klein J and Le Traon Y. (2023). Big Code Search: A Bibliography. ACM Computing Surveys. 56:1. (1-49). Online publication date: 31-Jan-2024.

    https://doi.org/10.1145/3604905

  • Wang Y, Zhou Y, Chen T, Zhang J, Yang W and Huang Z. (2022). Sequence-Aware API Recommendation Based on Collaborative Filtering. International Journal of Software Engineering and Knowledge Engineering. 10.1142/S0218194022500437. 32:08. (1203-1228). Online publication date: 1-Aug-2022.

    https://www.worldscientific.com/doi/10.1142/S0218194022500437

  • 闫 昭, 项 欣 and 李 泽. (2022). Item correlation modeling in interaction sequence for graph convolutional session recommendation. SCIENTIA SINICA Informationis. 10.1360/SSI-2020-0383. 52:6. (1069). Online publication date: 1-Jun-2022.

    https://engine.scichina.com/doi/10.1360/SSI-2020-0383

  • Kumar B and Bala P. (2017). Cosine based latent factor model for ranking the recommendation. Operational Research. 10.1007/s12351-017-0325-6. 20:1. (297-317). Online publication date: 1-Mar-2020.

    http://link.springer.com/10.1007/s12351-017-0325-6

  • Tornede A, Wever M and Hüllermeier E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. 10.1007/978-3-030-61527-7_21. (309-324).

    http://link.springer.com/10.1007/978-3-030-61527-7_21

  • Luu M and Lim E. (2018). Do your friends make you buy this brand?. Data Mining and Knowledge Discovery. 32:2. (287-319). Online publication date: 1-Mar-2018.

    https://doi.org/10.1007/s10618-017-0535-9

  • Xiao L, Min Z, Yiqun L and Shaoping M. (2017). A Neural Network Model for Social-Aware Recommendation. Information Retrieval Technology. 10.1007/978-3-319-70145-5_10. (125-137).

    http://link.springer.com/10.1007/978-3-319-70145-5_10

  • Cergani E, Proksch S, Nadi S and Mezini M. Addressing scalability in API method call analytics. Proceedings of the 2nd International Workshop on Software Analytics. (1-7).

    https://doi.org/10.1145/2989238.2989240

  • Ganser A, Lichter H, Roth A and Rumpe B. (2016). Staged model evolution and proactive quality guidance for model libraries. Software Quality Journal. 24:3. (675-708). Online publication date: 1-Sep-2016.

    https://doi.org/10.1007/s11219-015-9298-y

  • Xu Y and Yin J. (2015). Collaborative recommendation with user generated content. Engineering Applications of Artificial Intelligence. 45:C. (281-294). Online publication date: 1-Oct-2015.

    https://doi.org/10.1016/j.engappai.2015.07.012

  • Zhao T, McAuley J and King I. Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. (261-270).

    https://doi.org/10.1145/2661829.2661998

  • Krasnoshchok O and Lamo Y. (2014). Extended Content-boosted Matrix Factorization Algorithm for Recommender Systems. Procedia Computer Science. 10.1016/j.procs.2014.08.122. 35. (417-426).

    https://linkinghub.elsevier.com/retrieve/pii/S1877050914010874

  • Fan C, Lan Y, Guo J, Lin Z and Cheng X. Collaborative factorization for recommender systems. Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. (949-953).

    https://doi.org/10.1145/2484028.2484176

  • Zhu P and Yao Z. (2013). Cold-Start Collaborative Filtering Based on User Registration Process. The 19th International Conference on Industrial Engineering and Engineering Management. 10.1007/978-3-642-38427-1_124. (1175-1186).

    http://link.springer.com/10.1007/978-3-642-38427-1_124

  • Fan C and Lin Z. (2013). Collaborative Ranking with Ranking-Based Neighborhood. Web Technologies and Applications. 10.1007/978-3-642-37401-2_74. (770-781).

    http://link.springer.com/10.1007/978-3-642-37401-2_74

  • Karatzoglou A. Collaborative temporal order modeling. Proceedings of the fifth ACM conference on Recommender systems. (313-316).

    https://doi.org/10.1145/2043932.2043991

  • Karatzoglou A, Amatriain X, Baltrunas L and Oliver N. Multiverse recommendation. Proceedings of the fourth ACM conference on Recommender systems. (79-86).

    https://doi.org/10.1145/1864708.1864727