Collaborative Filtering Deep Recommendation Algorithm Based on Time Feature

doi:10.3778/j.issn.1002-8331.2106-0434

Abstract

Abstract: Aiming at the problem of data sparsity and cold start in recommendation algorithm, this paper proposes a convolutional neural network based collaborative filtering depth recommendation algorithm（CNN deep recommendation algorithm with time, C-DRAWT） and a multi-layer perceptron based collaborative filtering depth recommendation algorithm（MLP deep recommendation algorithm with time, M-DRAWT）. The algorithm first performs data preprocessing, and uses binary to encode user and project information, which alleviates the sparseness of books in one-hot encoding. Subsequently, the hidden features of the user and the item are extracted, and the features of the user and the item are combined with the timestamp feature, and then input into the optimized convolutional neural network and the multi-layer perceptron respectively, and finally the recommended item at the latest time is obtained. The two algorithms are verified by comparative experiments based on MovieLens-1M data set. The F1 score value and RMSE value are increased by 0.78% and 2.7% respectively. The results show that this method can alleviate the problems of data sparsity and cold start, and has better recommendation effect than the previous model.

Key words: recommendation algorithm, time characteristics, deep learning, convolutional neural network, multilayer perceptron

摘要： 针对推荐算法中的数据稀疏性和冷启动问题，提出了基于卷积神经网络的结合时间特征的协同过滤深度推荐算法（CNN-deep recommend algorithm with time，C-DRAWT）与基于多层感知机的结合时间特征的协同过滤深度推荐算法（MLP-deep recommend algorithm with time，M-DRAWT）。算法进行数据预处理，利用二进制来编码用户与项目的信息，缓解了one-hot编码的书籍稀疏性问题。提取出用户与项目的隐藏特征，将用户和项目的特征融合时间戳特征，分别输入到优化后的卷积神经网络和多层感知机进行，得到最新时刻的推荐项目。两个算法经过基于MovieLens-1M数据集的对比实验验证，得到的F1-Score值平均提高了0.78%，RMSE值平均提高了2.7%。结果表明，该方法能够缓解数据稀疏性和冷启动问题，相比较于之前的模型具有较好的推荐效果。

WEI Ziyu, ZHU Xiaodong, XU Yi. Collaborative Filtering Deep Recommendation Algorithm Based on Time Feature[J]. Computer Engineering and Applications, 2022, 58(23): 67-73.

魏紫钰, 朱小栋, 徐怡. 结合时间特征的协同过滤深度推荐算法[J]. 计算机工程与应用, 2022, 58(23): 67-73.

References

[1] 黄立威，江碧涛，吕守业，等.基于深度学习的推荐系统研究综述[J].计算机学报，2018，41（7）：1619-1647.
HUANG L W，JIANG B T，LV S Y，et al.Survey on deep learning based recommender systems[J].Chinese Journal of Computers，2018，41（7）：1619-1647.
[2] 王星凯，邓浩江，盛益强.基于深度学习的智能推荐系统综述[J].网络新媒体技术，2021，10（1）：1-11.
WANG X K，DENG H J，SHENG Y Q.A review of intelligent recommendation system based on deep learning[J].Journal of Network New Media，2021，10（1）：1-11.
[3] 冷亚军，陆青，梁昌勇.协同过滤推荐技术综述[J].模式识别与人工智能，2014，27（8）：720-734.
LENG Y J，LU Q，LIANG C Y.Survey of recommendation based on collaborative filtering[J].Pattern Recognition and Artificial Intelligence，2014，27（8）：720-734.
[4] LE W，XIANGNAN H，XIANG W，et al.A survey on neural recommendation：from collaborative filtering to content and context enriched recommendation[J].arXiv：2104.13030，2021.
[5] PORTEOUS I，ASUNCION A，WELLING M.Bayesian matrix factorization with side information and Dirichlet process mixtures[C]//Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence，Atlanta，Georgia，USA，July 11-15，2010.
[6] 王国霞，刘贺平.个性化推荐系统综述[J].计算机工程与应用，2012，48（7）：66-76.
WANG G X，LIU H P.Survey of personalized recommendation system[J].Computer Engineering and Applications，2012，48（7）：66-76.
[7] 张清，于博，王辉，等.一种有效缓解数据稀疏问题的协同过滤推荐算法[J].合肥工业大学学报（自然科学版），2019，42（4）：473-478.
ZHANG Q，YU B，WANG H，et al.An efficient recommendation algorithm for relieving data sparsity in colla-borative filtering[J].Journal of Hefei University of Technology（Natural Science），2019，42（4）：473-478.
[8] DUREDDY H V，KADEN Z.Handling cold-start colla-borative filtering with reinforcement learning[J].arXiv：1806. 06192，2018.
[9] WANG H，WANG N Y，YEUNG D Y.Collaborative deep learning for recommender systems[C]//Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，2015：1235-1244.
[10] LEI Z，VAHID N，PHILIP S Y.Joint deep modeling of users and items using reviews for recommendation[C]//Proceedings of the Tenth ACM International Conference on Web Search and Data Mining，2017：425-434.
[11] 李同欢，唐雁，刘冰.基于深度学习的多交互混合推荐模型[J].计算机工程与应用，2019，55（1）：135-141.
LI T H，TANG Y，LIU B.Multi-interaction hybrid recommendation model based on deep learning[J].Computer Engineering and Applications，2019，55（1）：135-141.
[12] 刘振鹏，尹文召，王文胜，等.HRS-DC：基于深度学习的混合推荐模型[J].计算机工程与应用，2020，56（14）：169-175.
LIU Z P，YIN W Z，WANG W S，et al.HRS-DC：hybrid recommendation model based on deep learning[J].Computer Engineering and Applications，2020，56（14）：169-175.
[13] 冯楚滢，司徒国强，倪玮隆.协同深度学习推荐算法研究[J].计算机系统应用，2019，28（1）：169-175.
FENG C Y，SITU G Q，NI W L.Research on collaborative deep learning recommendation algorithm[J].Computer Systems & Applications，2019，28（1）：169-175.
[14] 陈彬，张荣梅，张琦.DCFM：基于深度学习的混合推荐模型[J].计算机工程与应用，2021，57（3）：150-155.
CHEN B，ZHANG R M，ZHANG Q.DCFM：hybrid recommendation model based on deep learning[J].Computer Engineering and Applications，2021，57（3）：150-155.
[15] ALFARHOOD M，CHENG J.DeepHCF：a deep learning based hybrid collaborative filtering approach for recommendation systems[C]//Proc of 17th IEEE International Conference on Machine Learning and Applications（ICMLA），2018：89-96.
[16] 周传华，于猜，鲁勇.融合评分矩阵和评论文本的深度神经网络推荐模型[J].计算机应用研究，2021，38（4）：1058-1061.
ZHOU C H，YU C，LU Y.Recommendation model of deep neural network combining rating matrix and review text[J].Application Research of Computers，2021，38（4）：1058-1061.
[17] WU S，REN W，YU C，et al.Personal recommendation using deep recurrent neural networks in NetEase[C]//IEEE International Conference on Data Engineering，2016.
[18] WU C Y，AHMED A，BEUTEL A，et al.Recurrent recommender networks[C]//Tenth ACM International Conference on Web Search and Data Mining，2017.
[19] SUN P，WU L，WANG M.Attentive recurrent social recommendation[C]//41st International ACM SIGIR Conference on Research & Development in Information Retrieval，2018.
[20] 蔡念，刘广聪，蔡红丹.改进矩阵分解与卷积神经网络结合的推荐模型[J].计算机工程与应用，2019，55（19）：178-184.
CAI N，LIU G C，CAI H D.Improved model combining improved matrix decomposition and convolutional neural networks[J].Computer Engineering and Applications，2019，55（19）：178-184.
[21] SALAKHUTDINOV R，MNIH A.Probabilistic matrix factorization[C]//Proceedings of International Conference on Neural Information Processing Systems，2007.
[22] KIM D H，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.