针对异构隐式反馈的置信度估计方法

王静; 林兰芬; 张恒; 屠佳琪; 于鹏华

doi:10.1631/FITEE.1601468

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针对异构隐式反馈的置信度估计方法

常规文章 | Updated：2022-05-19

- 针对异构隐式反馈的置信度估计方法
- A novel confidence estimation method for heterogeneous implicit feedback
- 信息与电子工程前沿（英文） 2017年18卷第11期页码：1817-1827
- Affiliations：
  
  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  Jing WANG, E-mail: cswangjing@zju.edu.cn
- Funds：
  
  Project supported by the National Basic Research Program (973) of China (No. 2015CB352400) and the National Key Research and Development Program of China (No. 2016YFB1200203-03)
- DOI：10.1631/FITEE.1601468
  中图分类号： TP391
- 收稿：2016-08-11，
  
  修回：2017-11-24，
  
  纸质出版：2017-11
- Accepted：
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王静, 林兰芬, 张恒, 等. 针对异构隐式反馈的置信度估计方法[J]. 信息与电子工程前沿（英文）, 2017,18(11):1817-1827.

Jing WANG, Lan-fen LIN, Heng ZHANG, et al. A novel confidence estimation method for heterogeneous implicit feedback[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1817-1827.
王静, 林兰芬, 张恒, 等. 针对异构隐式反馈的置信度估计方法[J]. 信息与电子工程前沿（英文）, 2017,18(11):1817-1827. DOI： 10.1631/FITEE.1601468.

Jing WANG, Lan-fen LIN, Heng ZHANG, et al. A novel confidence estimation method for heterogeneous implicit feedback[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1817-1827. DOI： 10.1631/FITEE.1601468.

摘要

隐式反馈是指用户通过行为间接表达用户的意见。由于其在现实世界中的易得性和丰富性，越来越受到推荐系统领域的关注。使用隐式反馈的常见做法是把隐式反馈当做正面或者负面的用户偏好，并附带不同的置信度。这类方法大多认为，与隐式反馈相关的数值（例如观看的时间）反映了置信度的大小，并且数值越大代表置信度越大。这种假设只适合只有一种隐式反馈的场景。然而在现实世界中，经常同时存在多种隐式反馈，称为异构隐式反馈。现有方法无法有效地从异构隐式反馈中推测出置信度。本文提出了一种新颖的利用异构隐式反馈预测置信度的方法，把预测的置信度应用到"基于点的"和"基于对的"矩阵分解模型中，并提出了一种更通用的方法来为"基于对的"方法选择有效的训练样本。在天猫提供的真实电商数据集上的实验表明，本文方法在多个指标上的效果均要优于现有的方法。

Abstract

Implicit feedback

which indirectly reflects opinion through user behaviors

has gained increasing attention in recommender system communities due to its accessibility and richness in real-world applications. A major way of exploiting implicit feedback is to treat the data as an indication of positive and negative preferences associated with vastly varying confidence levels. Such algorithms assume that the numerical value of implicit feedback

such as time of watching

indicates confidence

rather than degree of preference

and a larger value indicates a higher confidence

although this works only when just one type of implicit feedback is available. However

in real-world applications

there are usually various types of implicit feedback

which can be referred to as heterogeneous implicit feedback. Existing methods cannot efficiently infer confidence levels from heterogeneous implicit feedback. In this paper

we propose a novel confidence estimation approach to infer the confidence level of user preference based on heterogeneous implicit feedback. Then we apply the inferred confidence to both point-wise and pair-wise matrix factorization models

and propose a more generic strategy to select effective training samples for pair-wise methods. Experiments on real-world e-commerce datasets from Tmall.com show that our methods outperform the state-of-the-art approaches

considering several commonly used ranking-oriented evaluation criteria.

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