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A polynomial modeling based algorithm in top-N recommendation

机译：top-N推荐中基于多项式建模的算法

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摘要

Recommendation is the process of identifying and recommending items that are more likely to be of interest to a user. Recommender systems have been applied in variety of fields including e-commerce web pages to increase the sales through the page by making relevant recommendations to users. In this paper, we pose the problem of recommendation as an interpolation problem, which is not a trivial task due to the high dimensional structure of the data. Therefore, we deal with the issue of high dimension by representing the data with lower dimensions using High Dimensional Model Representation (HDMR) based algorithm. We combine this algorithm with the collaborative filtering philosophy to make recommendations using an analytical structure as the data model based on the purchase history matrix of the customers. The proposed approach is able to make a recommendation score for each item that have not been purchased by a customer which potentiates the power of the classical recommendations. Rather than using benchmark data sets for experimental assessments, we apply the proposed approach to a novel industrial data set obtained from an e-commerce web page from apparels domain to present its potential as a recommendation system. We test the accuracy of our recommender system with several pioneering methods in the literature. The experimental results demonstrate that the proposed approach makes recommendations that are of interest to users and shows better accuracy compared to state-of-the-art methods. (C) 2017 Elsevier Ltd. All rights reserved.
机译：推荐是识别和推荐用户更可能感兴趣的项目的过程。推荐器系统已应用于包括电子商务网页在内的多个领域，以通过向用户提出相关建议来增加整个页面的销售额。在本文中，我们将推荐问题作为插值问题提出，由于数据的高维结构，这并不是一项琐碎的任务。因此，我们通过使用基于高维模型表示（HDMR）的算法以低维表示数据来解决高维问题。我们将此算法与协作过滤原理相结合，以基于客户购买历史矩阵的分析结构作为数据模型来提出建议。所提出的方法能够对尚未被顾客购买的每个物品做出推荐评分，这增强了经典推荐的力量。我们没有使用基准数据集进行实验评估，而是将建议的方法应用于从服装领域的电子商务网页中获得的新型工业数据集，以展示其作为推荐系统的潜力。我们用几种开创性的方法在文献中测试了推荐系统的准确性。实验结果表明，与最新方法相比，该方法提出了用户感兴趣的建议，并且显示出更高的准确性。 （C）2017 Elsevier Ltd.保留所有权利。

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