用户偏好与个性化推荐算法的优化

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"在线用户偏好和个性化推荐"的科研文章主要关注如何提升个性化推荐系统的准确性和多样性。作者提出了一种新的混合推荐算法，该算法结合了概率扩展和热传导两种不同的推荐策略，一种倾向于推荐热门商品，另一种则倾向于推荐冷门商品。通过可调参数，该方法能在系统级别上找到最佳的平衡点。文章指出，将这种混合方法应用到个人层面上，即每个用户都有自己的个性化混合参数进行调整，可以显著提高推荐效果。研究发现，用户的个性化参数与其对推荐系统的满意度负相关，而与他们平均收集项目的程度正相关。这意味着，用户的偏好和行为模式对于推荐算法的个性化设置至关重要。为了充分利用这一发现，研究人员提出了一个策略，根据用户的特点分配合适的个性化参数，进一步优化了原始的混合方法。这个策略旨在考虑到用户的异质性，因为不同用户对于推荐的敏感度和需求是不同的。通过这种方法，推荐系统的性能得到了显著提升，从而提高了用户体验和推荐的准确性。关键词包括"混合算法"、"推荐系统"、"个性化参数"和"用户异质性"，这表明该研究的核心是探索如何在推荐系统中结合多种算法并考虑用户个体差异，以提供更精准、更多元化的推荐服务。通过这样的个性化推荐，不仅可以提升用户满意度，也有助于提高在线平台的商品或服务销售效率。

Physica A 392 (2013) 3417–3423

Contents lists available at SciVerse ScienceDirect

Physica A

journal homepage: www.elsevier.com/locate/physa

Preference of online users and personalized

recommendations

Yuan Guan

, Dandan Zhao

, An Zeng

b,c,∗

, Ming-Sheng Shang

a,c,∗∗

Web Science Center, University of Electronic Science and Technology of China, Chengdu 611731, PR China

Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland

Institute of Information Economy, Alibaba Business School, Hangzhou Normal University, Hangzhou, 310036, PR China

h i g h l i g h t s

• We apply the hybrid recommendation method at individual level.

• Each user has his own personalized hybrid parameter to adjust.

• Users are found to be quite different in the optimal personalized hybrid parameters.

• We propose a strategy to assign users with suitable personalized parameters.

• The recommendation performance can be significantly improved.

a r t i c l e i n f o

Article history:

Received 14 November 2012

Received in revised form 27 January 2013

Available online 10 April 2013

Keywords:

Hybrid algorithm

Recommender system

Personalized parameter

User heterogeneity

a b s t r a c t

In a recent work [T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J.R. Wakeling, Y.-C. Zhang, Proc.

Natl. Acad. Sci. 107 (2010) 4511], a personalized recommendation algorithm with high

performance in both accuracy and diversity is proposed. This method is based on the

hybridization of two single algorithms called probability spreading and heat conduction,

which respectively are inclined to recommend popular and unpopular products. With a

tunable parameter, an optimal balance between these two algorithms in system level is

obtained. In this paper, we apply this hybrid method in individual level, namely each user

has his/her own personalized hybrid parameter to adjust. Interestingly, we find that users are

quite different in personalized hybrid parameters and the recommendation performance

can be significantly improved if each user is assigned with his/her optimal personalized

hybrid parameter. Furthermore, we find that users’ personalized parameters are negatively

correlated with users’ degree but positively correlated with the average degree of the

items collected by each user. With these understandings, we propose a strategy to assign

users with suitable personalized parameters, which leads to a further improvement of the

original hybrid method. Finally, our work highlights the importance of considering the

heterogeneity of users in recommendation.

1. Introduction

The last few years have witnessed serious problems of information explosion. The amount of information, especially on

the internet, is increasing far more quickly than our ability to process it. For instance, there are thousands of movies, millions

of books and billions of web pages. The ongoing rapid expansion of the Internet resource greatly increases the necessity of

∗

Corresponding author. Tel.: +41 026 3009275.

∗∗

Corresponding author at: Web Science Center, University of Electronic Science and Technology of China, Chengdu 611731, PR China.

E-mail addresses: an.zeng@unifr.ch (A. Zeng), Shang.mingsheng@gmail.com (M.-S. Shang).

http://dx.doi.org/10.1016/j.physa.2013.03.045

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