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首页> 外文会议>2017 15th International Conference on Quality in Research : International Symposium on Electrical and Computer Engineering >Recommender engine using cosine similarity based on alternating least square-weight regularization
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Recommender engine using cosine similarity based on alternating least square-weight regularization

机译：基于交替最小二乘正则化的余弦相似度的推荐引擎

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摘要

By the growth of digital data which leads to more complex demands from user to find the information or items. Search engines solve most of the problems but have the drawback, it depends on the query/term that the user enter. The problem appears when the user forget or does not know the query that associated with the items. The Recommendation comes as a solution to provide personal information by studying the interaction of a user, user community, and items that have been recorded previously. Collaborative filtering as a method to provide personalized recommendations based on other users who have similar tastes. However, the results of collaborative filtering tend random, sometimes users need an item with similar genre/subjects. This paper discusses a model of a recommendation engine for new users with a method of collaborative filtering based on genre similarly with the aim of giving the smallest error with high precision. First filter we use Alternating Least Square-Weight Regularization (ALS-WR) is selected as algorithms for collaborative filtering. Second filter we use Cosine Similarity is selected as an algorithm for genre similarity. We use datasets from movielens.org. The RMSE on the first recommendation generated is 0.89 for 100K ratings, 0.86 for the 1M ratings, and 0.81 for the 10M rating. By iterative and training on larger data, it will make a better model, so RMSE can be smaller. They are concluded that ALS-WR able to deliver adaptive, with regulatory parameters that can be controlled and adjusted. The more data but the error on the wane, that is means this algorithm is suitable for growing data or big data. The item that has been sorted with the ALS-WR algorithm, letter approximated with cosine similarity, and with only 10 items movie displays with the highest degree of similarity, that be able to generate high precision.
机译：随着数字数据的增长，导致用户寻找信息或物品的需求更加复杂。搜索引擎解决了大多数问题，但有缺点，这取决于用户输入的查询/术语。当用户忘记或不知道与项目关联的查询时，将出现问题。该建议书是通过研究用户，用户社区和先前记录的项目的交互来提供个人信息的解决方案。协作过滤是一种基于具有相似爱好的其他用户提供个性化推荐的方法。但是，协作过滤的结果往往是随机的，有时用户需要具有类似类型/主题的项目。本文以基于流派的协作过滤方法为基础，讨论了一种针对新用户的推荐引擎模型，旨在以最小的误差给出高精度的结果。我们使用交替最小二乘正则化（ALS-WR）的第一个过滤器被选作协作过滤的算法。选择我们使用余弦相似度的第二个滤波器作为体裁相似度算法。我们使用来自movielens.org的数据集。生成的第一个推荐的RMSE对于100K等级为0.89，对于1M等级为0.86，对于10M等级为0.81。通过迭代和训练更大的数据，它将形成更好的模型，因此RMSE可以更小。他们的结论是，ALS-WR能够提供自适应性，并具有可以控制和调节的调节参数。数据越多，但误差就越小，这意味着该算法适用于增长数据或大数据。已使用ALS-WR算法排序的项目，具有近似余弦相似度的字母以及只有10个项目的相似度最高的电影显示能够产生高精度。

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