Hybrid Recommendation with Category Preferences and Item Timeliness Factor
Yang Huaizhen1,Zhang Jing1,Li Lei2()
1Business School, Guilin University of Electronic Technology, Guilin 541004, China 2Business School, Guilin University of Technology, Guilin 541004, China
[Objective] This paper addresses the influence of historical data sparsity, category preference, and item timeliness on the performance of recommendation algorithms and improves their accuracy. [Methods] Firstly, we used Huffman Coding to encode the rating data with category preference and item popularity. Then, we computed the score similarity matrices of users and projects. We also extracted their latent feature vectors using the DeepWalk model. Finally, we fused the user and project feature vectors and predicted the project ratings with Extreme Learning Machines. [Results] We examined the new model on the MovieLens and Yahoo! R3 datasets. As the proportion of the training set increased, the highest prediction accuracies reached 95.52% and 98.01%, respectively, with a runtime of only 19.93s and 22.21s. The proposed algorithm outperformed the XGB-CF algorithm in terms of prediction by 0.84 and 2.10 percentage points, respectively, with a runtime reduction of 7.92s and 9.79s. [Limitations] The proposed algorithm did not consider the textual information from user comments and diversified project categories. [Conclusions] Our new algorithm demonstrates higher prediction accuracy than the reference algorithm and can be used for personalized recommendations.
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