## 6 Simulation Experiments
This is due to the fact that under the uniform distribution, the hit rate decreases rapidly with the increase in the number of contents \(n\), whereas it does not under the zipf distribution. In fact, this is due to the fact that under the uniform distribution, all user preferences are completely randomized, and the more content there is, the harder it is to find common preferences. Under the zipf distribution, on the other hand, the higher the ranking, the higher the probability that the content is preferred by the user, and the user's preferences tend to be similar, so that an increase in the number of contents has less impact on finding the public preference content. The relationship between cache hit rate and JSD and the number of recommended content \(k\) is shown in Figures 8 and 9 for different \(\alpha\) and \(\gamma\), respectively. Its experimental parameters are \(n=200\), \(m=50\), \(c=200\), and \(d_{j}=0.3\). Figure 8 suggests that the hit rate increases with k but no longer increases when it reaches a certain magnitude, which is shown in the figure around \(k=100\). The effect of the number of recommended contents on the increase in hit rate is not significant. It can be observed that for \(\alpha=0.1\) and \(\gamma=0.5\), there is almost no improvement. However, when \(\alpha=10\) and \(\gamma=10\), the number of recommended content \(k\) is able to bring a large improvement. The fundamental reason for this is that smaller \(\alpha\) and \(\gamma\) limit the distortion of user preferences by the recommender system, and the recommender system is less useful. Therefore, in order to ensure the effectiveness of improving the number of recommendations \(k\), both \(\alpha\) and \(\gamma\) should be improved. On the other hand, when \(\alpha\) and \(\gamma\) are small, the growth trend of JSD is the same as the growth trend of hit rate, and both increase with \(k\) until convergence. Notice that when \(\alpha=10\) and \(\gamma=10\), the JSD shows a trend of increasing and then decreasing. This indicates that in the first half of the curve, where the hit rate increases rapidly with increasing k, the recommender system is limited by the number of recommendations \(k\), and the increase of \(k\) liberates the recommender system's recommending ability and brings about a rapid increase in the hit rate and JSD. In the second half of the curve, where the hit rate stabilizes as \(k\) increases, an increase in the number of recommendations \(k\) does not lead to an increase in the hit rate but reduces the distortion of user preferences. In effect, because more content can be recommended, the recommender system does not have to  
Figure 7: Changes in hit rate with increasing number of content \(n\) for different server capacities and user preference distributions. overly distort certain users' preferences for certain content, which brings about a decrease in JSD. In summary, we experimentally verified that our algorithm is greatly improved over the LFU algorithm and that the hit rate will increase as the optional recommendation set of the algorithm increases with an increase in the cache size c or \(k\) value. We observe that an increase in the number of contents, n, makes it harder to find users' public preferences and thus reduces the hit rate, which is more serious in the case of a uniform distribution with completely randomized user preferences. In addition, the parameters \(\gamma\) and \(\alpha\) can directly determine the degree of distortion of the recommender system with respect to the user's preferences, which together determine the constraints on the recommendation score; the larger the value, the larger the hit rate of the algorithm, but the larger the distortion value of the user's preferences. On the other hand, if the parameters \(\gamma\) and \(\alpha\) are large enough, increasing the number of recommendations \(k\) can simultaneously improve the hit rate and reduce the distortion of user preferences. To ensure the user experience, setting too large \(\gamma\) and \(\alpha\) should be restricted.