Multi-Objective Recommendation for Massive Remote Teaching Resources

Abstract

In remote teaching, massive resource data types have heterogeneous diversity attributes. Currently, recommendation algorithms only consider the optimal solution in the local domain under an attention mechanism to ensure efficiency, without considering the embedding correlation of recommendation features in the entire local domain, resulting in suboptimal recommendation results in a massive data environment. This paper proposes an improved multi-objective intelligent recommendation algorithm for massive remote teaching resources. The logical framework of a multi-objective intelligent recommendation algorithm for massive resources is provided. First, connections between different domains are constructed through knowledge graphs as well as global domain embedding are generated related to users and remote teaching resources. Then, recommendation representations of users and teaching resources in the target domain are expressed through fully localized embedding representations. Finally, the recommendation representation is trained through the output layer to output the target domain recommendation prediction score for remote teaching resources. The average and diversity of remote teaching resource prediction scores are used as evaluation parameters for the recommendation list, and a multi-objective optimization algorithm is adopted to optimize the calculation process of recommendation prediction scores through operations such as crossover and mutation of initial solutions. A new prediction score of remote teaching resource recommendation is generated and compared with existing methods to obtain a better recommendation list. Experimental results show that the MRR values of the recommended results of this method are all above 0.985, and the MAE value is controlled below 0.5. The recommended results are accurate and can effectively improve the teaching performance of students in different majors, improve prediction scores, diversity scores, and satisfaction.