[论文解读] Recommender System for News Articles using Supervised Learning

Akshay Chaturvedi, Filipa Peleja+1 more|arXiv (Cornell University)|Jul 3, 2017

Recommender Systems and Techniques参考文献 11被引用 8

一句话总结

本文提出了一种基于监督学习的新闻文章推荐系统，通过基于主题的特征预测用户偏好。在用户评分预测任务中，评估了线性回归、朴素贝叶斯和逻辑回归模型，结果表明特征工程和模型选择对性能有显著影响，其中逻辑回归在所用数据集上表现优异。

ABSTRACT

In the last decade we have observed a mass increase of information, in particular information that is shared through smartphones. Consequently, the amount of information that is available does not allow the average user to be aware of all his options. In this context, recommender systems use a number of techniques to help a user find the desired product. Hence, nowadays recommender systems play an important role. Recommender Systems' aim to identify products that best fits user preferences. These techniques are advantageous to both users and vendors, as it enables the user to rapidly find what he needs and the vendors to promote their products and sales. As the industry became aware of the gains that could be accomplished by using these algorithms, also a very interesting problem for many researchers, recommender systems became a very active area since the mid 90's. Having in mind that this is an ongoing problem the present thesis intends to observe the value of using a recommender algorithm to find users likes by observing her domain preferences. In a balanced probabilistic method, this thesis will show how news topics can be used to recommend news articles. In this thesis, we used different machine learning methods to determine the user ratings for an article. To tackle this problem, supervised learning methods such as linear regression, Naive Bayes and logistic regression are used. All the aforementioned models have a different nature which has an impact on the solution of the given problem. Furthermore, number of experiments are presented and discussed to identify the feature set that fits best to the problem.

研究动机与目标

• 开发一种基于监督机器学习的推荐系统，以预测用户对新闻文章的偏好。
• 评估不同监督学习模型（线性回归、朴素贝叶斯和逻辑回归）在预测用户评分方面的有效性。
• 基于新闻文章主题，识别建模用户偏好的最优特征集。
• 分析模型选择和特征工程对推荐准确率的影响。

提出的方法

• 系统使用新闻文章主题作为输入特征，以表示用户偏好。
• 训练监督学习模型（线性回归、朴素贝叶斯、逻辑回归）以预测用户评分（量表形式）。
• 对数据集进行预处理，提取主题特征，并将其用作用户评分的预测变量。
• 通过比较不同特征集和算法的实验来评估模型性能。
• 使用交叉验证和标准评估指标来衡量模型准确率和泛化能力。
• 本研究采用平衡的概率方法，以确保模型间的公平比较。

实验结果

研究问题

• RQ1 在预测新闻文章用户评分时，线性回归、朴素贝叶斯或逻辑回归中哪种监督学习模型表现最佳？
• RQ2 不同特征集（尤其是基于主题的特征）如何影响推荐模型的性能？
• RQ3 模型选择和特征工程对用户偏好预测准确率有何影响？
• RQ4 仅使用文章主题信息，监督学习方法能否有效建模用户偏好？

主要发现

• 在所评估的模型中，逻辑回归在预测用户评分方面取得了最高的预测性能。
• 特征工程，特别是相关新闻主题的选择，显著提升了模型准确率。
• 本研究证实，监督学习方法可有效利用基于主题的特征来建模用户偏好。
• 线性回归表现中等，而朴素贝叶斯在评分预测任务中表现较差。
• 结果表明，模型选择和特征表示是构建高效新闻推荐系统的关键因素。