基于用户经验水平的推荐方法研究

发布时间：2018-06-01 03:23

本文选题：用户偏好 + 时间动态　；参考：《清华大学》2015年硕士论文

【摘要】：在推荐系统中,用户对产品的偏好往往会随着时间而发生动态的变化。从用户个人的角度出发,这意味着用户在不断与产品相接触的过程中积累了越来越多的知识与经验,从“新手”不断地向“专家”发展,从而使自己具有了不同的品位。例如,一位电影领域的“新手”用户可能会认为《黑客帝国》甚至有些无聊,而当他看过许多电影之后重新审视这部电影时,才能更好地理解并欣赏这部电影,从而给出更高的评价。同时,用户在推荐平台上给出评分时,可能会受到两种因素的即时性影响,分别为产品的平均得分与之前其他若干用户对产品的评分。例如,若某一用户试图为某一产品给出5分,但当他发现该产品的平均得分不到3分,或者在他之前的若干用户给出了1~2分的低分时,他可能会降低自己所给出的分数。为了掌握用户经验水平的发展模式,同时发现用户在评分时受到其他用户影响的机制,本文主要研究如何将隐马尔可夫模型与协同过滤算法相结合,以提升推荐系统的准确度,发现用户的行为模式,为电子商务中的精准营销提供理论支持。首先,在总结前人工作的基础上,基于所要解决问题的特征,本文提出了将隐马尔可夫模型与协同过滤算法相结合,来描述用户经验等级发展的模型。此后,在此模型的基础上,又添加了描述其他用户影响的参数,旨在研究用户在评分时受到其他用户影响的机制。同时,本文在四个推荐系统相关的数据集上进行了实验。实验结果表明,本文所提出的模型在预测准确率上优于前人所提出的模型。同时,通过对模型优化与参数学习结果的分析,也发现了一些有意义的结论。本研究在提升预测准确率的同时,在一定程度上优化了前人工作中所存在的一些不足,同时将隐马尔可夫模型应用于推荐系统领域,在理论上丰富了推荐系统的算法。同时,本文所进行的对于用户偏好动态的研究以及对于用户经验等级发展的建模机制,对于多指标推荐问题、专家发现与专家推荐问题等也具有一定的启示意义。
[Abstract]:In recommendation systems, users' preferences for products often change dynamically over time. From the individual point of view of the user, this means that the user has accumulated more and more knowledge and experience in the process of continuous contact with the product, and has developed from "novice" to "expert", so that he has different taste. For example, a "novice" user of the film industry may think the Matrix is even boring, and when he has seen many movies and revisited the movie, he can better understand and appreciate the film. Thus giving a higher evaluation. At the same time, the users may be affected by two kinds of factors, which are the average score of the product and the score of the other users when they give the rating on the recommendation platform. For example, if a user tries to give a product a score of 5, but finds that the average score of the product is less than 3, or several users before him give a low score of 1 to 2, he may reduce his score. In order to grasp the development mode of user experience level and find out the mechanism that users are influenced by other users in scoring, this paper mainly studies how to combine hidden Markov model with collaborative filtering algorithm to improve the accuracy of recommendation system. Discover the user's behavior pattern and provide theoretical support for accurate marketing in e-commerce. First of all, based on the previous work and the characteristics of the problem to be solved, this paper proposes a model that combines hidden Markov model with collaborative filtering algorithm to describe the development of user experience level. Then, on the basis of this model, parameters describing the influence of other users are added to study the mechanism by which users are influenced by other users. At the same time, the experiment is carried out on the data sets of four recommendation systems. The experimental results show that the prediction accuracy of the proposed model is better than that of the previous model. At the same time, by analyzing the results of model optimization and parameter learning, some meaningful conclusions are also found. This study not only improves the accuracy of prediction, but also optimizes some shortcomings of previous work to some extent. At the same time, the hidden Markov model is applied to the field of recommendation system, which enriches the algorithm of recommendation system in theory. At the same time, the research on user preference dynamics, the modeling mechanism for the development of user experience level, the multi-index recommendation problem, the expert discovery and the expert recommendation problem also have certain enlightenment significance.
【学位授予单位】：清华大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F724.6

【引证文献】