随着互联网和计算机技术的快速发展，在线学习逐渐成为很多学习者的学习方式。在线学习中的自适应学习材料推荐策略可以根据学习者的个人特征推荐相适应的学习材料，逐渐成为学术研究的热点。但是，自适应学习推荐策略研究中仍然存在一些问题。首先，目前的研究目标都在提高学习材料推荐的匹配度，这样容易造成过度匹配的问题，导致推荐的材料过于单一，多样性不足。其次，目前的研究中缺少对学习材料推荐多样性的定义，也没有能够平衡推荐匹配度和多样性的策略。

为了解决以上问题，本文通过改进基本的粒子群算法，建立了一种考虑多样性的自适应学习材料推荐策略。本文分为两个研究。研究一进行推荐策略的模型构建，首先参考相关文献，选取了一部分学习者特征与学习材料特征。之后，研究进一步设定了推荐规则，构建了匹配度惩罚函数。针对多样性不足的问题，研究构建了多样性惩罚函数，并且在算法流程的设计中，改进了粒子群算法，提出了一种新的位置更新方式。算法在迭代结束后加入了多样性转换的步骤，并融合多样性因子，用以平衡推荐的匹配度和多样性。研究二进行推荐策略的效果验证，实验了在不同学习材料数量，不同推荐材料序列长度的情况下，改进粒子群算法、基本粒子群算法和随机算法各自在匹配度和多样性上的推荐结果。此外，研究二还考察了在不同多样性因子下，改进粒子群算法的
迭代过程与推荐效果。                                                                                                      研究表明，在不同学习材料数量下，使用改进粒子群算法得到的推荐结果，相比随机算法与基本粒子群算法，匹配度和多样性都更好。在不同推荐材料序列长度下，使用改进粒子群算法得到的推荐匹配度和多样性都要优于随机算法和基本粒子群算法。并且随着推荐序列长度的增加，优势会更加明显。通过控制改进粒子群算法中多样性因子的大小，可以有效平衡推荐策略的匹配度和多样性。多样性因子越大，则推荐的多样性效果越好，但同时也会牺牲一定的匹配度。

总的来说，本文提出的考虑多样性的自适应学习材料推荐策略，可以使推荐的匹配度和多样性都能得到较好的结果，具体一定的科学与合理性。

        With the rapid development of the Internet and computer technology, online learning has gradually become a way of learning for many learners. Learning material recommendation strategy in online learning: it has gradually become a hot topic in academic research to recommend suitable learning materials according to learners' personal characteristics. However, there are still some problems in relevant studies at present. First, the current research objective is to improve the matching degree of learning material recommendation, which is easy to cause the problem of over-matching, resulting in the recommended materials are too single and lack of diversity. Secondly, current studies lack the definition of the diversity of a group of learning materials, and there is no strategy to balance the matching degree and diversity of recommendations.

        In order to solve the above problems, this paper establishes an adaptive learning material recommendation strategy considering diversity by improving the basic particle swarm optimization algorithm. This paper is divided into two studies. In the first study, the model of recommendation strategy is constructed. Firstly, some learner characteristics and learning material characteristics are selected by referring to relevant literatures. Then, the recommendation rules are further set up and the matching penalty function is constructed. In order to solve the problem of insufficient diversity, the diversity penalty function is constructed, and in the design of algorithm flow, the particle swarm optimization algorithm is improved, and a new position updating method is proposed. At the end of iteration, diversity transformation steps are added and diversity factors are fused to balance the matching degree and diversity of recommendations. In the second study, the effect of the recommendation strategy was verified, and the recommendation results of the improved particle swarm optimization algorithm, the elementary particle swarm optimization algorithm and the random algorithm on the matching degree and diversity were tested under the condition of different learning materials quantity and different recommended material sequence length. In addition, the second study also investigated the improved particle swarm optimization algorithm under different diversity factors iterative process and recommendation effect.

      The research shows that the recommendation results obtained by the improved particle swarm optimization algorithm have better matching degree and diversity than those obtained by the random algorithm and the basic particle swarm optimization algorithm under different amounts of learning materials. The results show that the improved particle swarm optimization  is better than the random algorithm and the basic particle swarm optimization . And with the increase of recommended sequence length, the advantage will be more obvious. By controlling the diversity factor in the improved particle swarm optimization algorithm, the matching degree and diversity of recommendation strategies can be effectively balanced. The greater the diversity factor is, the better the diversity effect of the recommendation will be, but at the same time, certain matching degree will be sacrificed.

      In general, the adaptive learning material recommendation strategy proposed in this paper considering diversity can make the matching degree and diversity of recommendations get better results, which is scientific and reasonable to a certain extent.