# A Personalized Learning Path Recommendation Method Incorporating Multi-Algorithm
## 3 Methodology
### Generating Knowledge Point Sequences  
The apriori algorithm is one of the classical algorithms of association rules. In 1993, [PERSON] et al. proposed a basic algorithm for identifying frequent item sets. The principle of the apriori algorithm is to use an iterative method called layer-by-layer search, where the k item set is used to explore the k + 1 item set [16]. In this paper, we use the apriori algorithm to mine the learning results of learners on an online learning platform, to determine the frequent item sets of knowledge points that learners do not master, and then to combine an existing knowledge map library to reasonably plan the sequence of knowledge points suitable for learners. The algorithm mainly obtains the frequent patterns we need from a huge dataset according to the given minimum support and confidence. Based on frequently received criteria, links between individual data can be found. The layer-by-layer iterative method was adopted by [PERSON] [17]. The generation of knowledge point sequences is supported by learners' existing cognitive levels and knowledge map databases. The set \(kg=\{k_{1},k_{2},\ldots,k_{n}\}\) is the set of \(n\) knowledge points in the knowledge map library, and the points are arranged according to the relationship between knowledge points. Set \(cg=\{c_{1},c_{2},\ldots,c_{n}\}\) contains learners' cognitive levels, specifically obtained by the Fuzzy-CDF model according to the learners' test question library. To obtain a set of knowledge points that learners do not master, the threshold \(\mu_{cg}\) is set, and the cognitive level is lower than the \(\mu_{cg}\) value, which refers to the knowledge points that are not mastered. The set \(ng=\{n_{1},n_{2},\ldots,n_{m}\}\) represents the set of knowledge points that are not mastered, and \(0\leq m\leq n,ng\subset kg\). For learners whose target knowledge point is \(B\), if they want to obtain knowledge point \(A\) and its association rule \(A\Rightarrow B\), that is, if learners do not master knowledge point \(A\), they will not master the target knowledge point, where \(A\subset ng\), \(B\subset ng\) or \(B\subset kg\), but \(A\cap B=\varnothing\). According to the principle of the apriori algorithm, frequent item sets need to be obtained according to support and confidence where support indicates the proportion of the dataset containing the current item set, and confidence indicates the probability of the occurrence of one item set when another item set occurs. If an item set meets the minimum support threshold, it is a frequent item set. If the item set also meets the minimum confidence threshold, it has strong association rules. Junior high school mathematics is used as an example to build a knowledge map of 584 knowledge points in the discipline. For any learner \(L_{i}\) on the online learning platform, some answer records of the learner in the test question library are shown in Table 2. The threshold of cognitive level \(\mu_{cg}=0.9\). Those in the \(cg\) set that are lower than this threshold are knowledge points that are not mastered. The minimum support is \(\mathrm{minsupport}=0.53\), and the minimum confidence is \(\mathrm{minconfindence}=0.91\). The obtained frequent item set of knowledge points that are not mastered is shown in Table 3.  
We obtain that the frequent item set of learner \(L_{i}\) is \(fq=\{1,\ 2,\ 3,\ 4,\ 7,\ 10,\ 17,\ 19,\ 21,\ 25\}\). When learners input target knowledge points, it is necessary to generate a sequence for learners to reach target knowledge points, that is, the learning sequence from \(fq\) to target knowledge \(n\) points. In the knowledge map, there is a certain dependency between two knowledge points. Therefore, this paper introduces a correlation to improve the accuracy of the knowledge point sequence.