|
|
|
| Deep Collaborative Filtering Algorithm with Embedding Implicit Similarity Groups |
Li Zhenyu,Li Shuqing( ) |
| College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210023, China |
|
|
|
|
Abstract [Objective] This paper tries to construct a deep collaborative filtering model that can capture local relevance as well as explicit / implicit feedbacks. [Methods] In the explicit recommendation tasks, we embedded similar groups found by implicit feedback search. Then, we create models for user-item group, user-similar-item group, and item-similar-user group with Multi-Layer Perceptron. [Results] We examined the new algorithm with MovieLens datasets. Compared with existing methods, the MAE and RMSE of our model were reduced by 10.94% and 11.79% respectively. [Limitations] More research is needed to identify the optimal number of the nearest neighbors for different datasets. [Conclusions] The new model could more effectively generate the recommendation results.
|
|
Received: 08 February 2021
Published: 23 December 2021
|
|
|
| Fund:Major Natural Science Research Projects of Colleges and Universities in Jiangsu Province of China(19KJA510011) |
|
Corresponding Authors:
Li Shuqing,ORCID：0000-0001-9814-5766
E-mail: leeshuqing@163.com
|
| [1] |
Ricci F, Rokach L, Shapira B. Recommender Systems: Introduction and Challenges[A]//Recommender Systems Handbook[M]. Boston, MA: Springer, 2015: 1-34.
|
| [2] |
Lu J, Wu D S, Mao M S, et al. Recommender System Application Developments: A Survey[J]. Decision Support Systems, 2015, 74:12-32.
doi: 10.1016/j.dss.2015.03.008
|
| [3] |
Goldberg K, Roeder T, Gupta D, et al. Eigentaste: A Constant Time Collaborative Filtering Algorithm[J]. Information Retrieval, 2001, 4(2):133-151.
doi: 10.1023/A:1011419012209
|
| [4] |
Koren Y, Bell R, Volinsky C. Matrix Factorization Techniques for Recommender Systems[J]. Computer, 2009, 42(8):30-37.
|
| [5] |
Xue H J, Dai X Y, Zhang J B, et al. Deep Matrix Factorization Models for Recommender Systems[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017: 3203-3209.
|
| [6] |
Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann Machines for Collaborative Filtering[C]// Proceedings of the 24th International Conference on Machine Learning. 2007: 791-798.
|
| [7] |
Sedhain S, Menon A K, Sanner S, et al. AutoRec: Autoencoders Meet Collaborative Filtering[C]// Proceedings of the 24th International Conference on World Wide Web. 2015: 111-112.
|
| [8] |
Liang H Z, Baldwin T. A Probabilistic Rating Auto-Encoder for Personalized Recommender Systems[C]// Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015: 1863-1866.
|
| [9] |
Li S, Kawale J, Fu Y. Deep Collaborative Filtering via Marginalized Denoising Auto-encoder[C]// Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 2015: 811-820.
|
| [10] |
Ouyang Y X, Liu W Q, Rong W G, et al. Autoencoder-Based Collaborative Filtering[C]// Proceedings of International Conference on Neural Information Processing. Springer, Cham, 2014: 284-291.
|
| [11] |
Kim D, Park C, Oh J, et al. Convolutional Matrix Factorization for Document Context-Aware Recommendation[C]// Proceedings of the 10th ACM Conference on Recommender Systems. 2016: 233-240.
|
| [12] |
Singhal A, Sinha P, Pant R. Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works[OL]. arXiv Preprint, arXiv: 1712.07525.
|
| [13] |
He X N, Liao L Z, Zhang H W, et al. Neural collaborative filtering[C]// Proceedings of the 26th International Conference on World Wide Web. 2017: 173-182.
|
| [14] |
Zheng Y, Liu C L, Tang B S, et al. Neural Autoregressive Collaborative Filtering for Implicit Feedback[C]// Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 2016: 2-6.
|
| [15] |
Chen W Y, Cai F, Chen H H, et al. Joint Neural Collaborative Filtering for Recommender Systems[J]. ACM Transactions on Information Systems, 2019, 37(4):1-30.
|
| [16] |
He X N, Du X, Y Wang X, et al. Outer Product-Based Neural Collaborative Filtering[OL]. arXiv Preprint, arXiv: 1808.03912.
|
| [17] |
Bai T, Wen J R, Zhang J, et al. A Neural Collaborative Filtering Model with Interaction-Based Neighborhood[C]// Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017: 1979-1982.
|
| [18] |
Ebesu T, Shen B, Fang Y. Collaborative Memory Network for Recommendation Systems[C]// Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018: 515-524.
|
| [19] |
Deng Z H, Huang L, Wang C D, et al. DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System[C]// Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1):61-68.
|
| [20] |
Koren Y. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model[C]// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008: 426-434.
|
| [21] |
Liu N N, Xiang E W, Zhao M, et al. Unifying Explicit and Implicit Feedback for Collaborative Filtering[C]// Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010: 1445-1448.
|
| [22] |
Xie R B, Ling C, Wang Y L, et al. Deep Feedback Network for Recommendation[C] // Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2020.
|
| [23] |
Zamani H, Croft W B. Learning a Joint Search and Recommendation Model from User-Item Interactions[C]// Proceedings of the 13th International Conference on Web Search and Data Mining. 2020: 717-725.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
