补充：基于项目的协同过滤推荐算法(Item-Based Collaborative Filtering Recommendation Algorithms)

前言

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相关工作

In this section we briefly present some of the research literature related to collaborative filtering, recommender systems, data mining and personalization.

在本节中，我们简要介绍了一些与协同过滤、推荐系统、数据挖掘和个性化相关的研究文献。

Tapestry [10] is one of the earliest implementations of collaborative filtering-based recommender systems. This system relied on the explicit opinions of people from a close-knit community, such as an office workgroup. However, recommender system for large communities cannot depend on each person knowing the others. Later, several ratings-based automated recommender systems were developed. The GroupLens research system [19,16] provides a pseudonymous collaborative filtering solution for Usenet news and movies. Ringo[27] and Video Recommender[14] are email and webbased systems that generate recommendations on music and movies, respectively. A special issue of Communications of the ACM[20] presents a number of different recommender systems.

Tapestry[10]是最早实现的基于协同过滤的推荐系统之一。这个系统依赖于来自一个紧密联系的社区的人们的明确意见，比如一个办公室工作组。然而，大型社区的推荐系统不能依赖于每个人都相互了解。后来，几个基于评分的自动推荐系统被开发出来。GroupLens研究系统[19,16]为Usenet新闻和电影提供了一个假名协同过滤解决方案。Ringo[27]和Video Recommender[14]分别是基于电子邮件和网络生成音乐和电影推荐的系统。ACM[20]的通信专刊介绍了一些不同的推荐系统。

Other technologies have also been applied to recommender systems, including Bayesian networks, clustering, and Horting. Bayesian networks create a model based on a training set with a decision tree at each node and edges representing user information. The model can be built off-line over a matter of hours or days. The resulting model is very small, very fast, and essentially as accurate as nearest neighbor methods [6]. Bayesian networks may prove practical for environments in which knowledge of user preferences changes slowly with respect to the time needed to build the model but are not suitable for environments in which user preference models must be updated rapidly or frequently.

其他技术也被应用于推荐系统，包括贝叶斯网络、聚类和Horting。贝叶斯网络基于训练集创建一个模型，其中每个节点和边代表用户信息的决策树。该模型可以在几小时或几天内离线构建。由此产生的模型非常小，非常快，基本上与最近邻方法[6]一样准确。贝叶斯网络可能被证明是实用的，在这种环境中，用户偏好的知识相对于建立模型所需的时间变化缓慢，但不适合用户偏好模型必须快速或频繁更新的环境。

Clustering techniques work by identifying groups of users who appear to have similar preferences. Once the clusters are created, predictions for an individual can be made by av- eraging the opinions of the other users in that cluster. Some clustering techniques represent each user with partial participation in several clusters. The prediction is then an aver- age across the clusters, weighted by degree of participation. Clustering techniques usually produce less-personal recommendations than other methods, and in some cases, the clusters have worse accuracy than nearest neighbor algorithms [6]. Once the clustering is complete, however, performance can be very good, since the size of the group that must be analyzed is much smaller. Clustering techniques can also be applied as a "first step" for shrinking the candidate set in a nearest neighbor algorithm or for distributing nearestneighbor computation across several recommender engines. While dividing the population into clusters may hurt the accuracy or recommendations to users near the fringes of their assigned cluster, pre-clustering may be a worthwhile trade-off between accuracy and throughput.

聚类技术通过识别具有相似偏好的用户组来工作。一旦创建了集群，就可以通过对该集群中其他用户的意见进行平均来对个人进行预测。有些聚类技术将每个用户表示为部分参与多个聚类。然后，预测是通过参与程度加权的簇的平均值。聚类技术通常比其他方法产生更少的个性化推荐，在某些情况下，聚类的准确性比最近邻算法[6]更差。然而，一旦聚类完成，性能就会非常好，因为要分析的分组的规模要小得多。聚类技术也可以作为缩小最近邻算法候选集的“第一步”，或者在多个推荐引擎中分布最近邻计算。虽然将群体划分为簇可能会影响准确性或对用户所分配簇的边缘的推荐，但预聚类可能是准确性和吞吐量之间的一个值得权衡的问题。

Horting is a graph-based technique in which nodes are users, and edges between nodes indicate degree of similarity between two users [1]. Predictions are produced by walking the graph to nearby nodes and combining the opinions of the nearby users. Horting differs from nearest neighbor as the graph may be walked through other users who have not rated the item in question, thus exploring transitive relationships that nearest neighbor algorithms do not consider. In one study using synthetic data, Horting produced better predictions than a nearest neighbor algorithm [1].

Horting是一种基于图的技术，其中节点是用户，节点之间的边表示两个用户之间的相似程度[1]。预测是通过游走图到附近的节点并结合附近用户的意见来产生的。Horting与最近邻算法的不同之处在于，它可能会遍历没有对所讨论的项目进行评分的其他用户，从而探索最近邻算法没有考虑的传递关系。在一项使用合成数据的研究中，Horting比最近邻算法[1]产生了更好的预测。

Schafer et al., [26] present a detailed taxonomy and examples of recommender systems used in E-commerce and how they can provide one-to-one personalization and at the same can capture customer loyalty. Although these systems have been successful in the past, their widespread use has exposed some of their limitations such as the problems of sparsity in the data set, problems associated with high dimensionality and so on. Sparsity problem in recommender system has been addressed in [23,11]. The problems associated with high dimensionality in recommender systems have been discussed in [4], and application of dimensionality reduction techniques to address these issues has been investigated in [24].

Schafer等人，[26]介绍了电子商务中推荐系统的详细分类和例子，以及它们如何提供一对一的个性化，同时可以捕获客户忠诚度。尽管这些系统在过去取得了成功，但它们的广泛应用也暴露了一些局限性，如数据集稀疏性问题、高维相关问题等。推荐系统中的稀疏性问题在[23,11]中得到了解决。在[4]模型中讨论了推荐系统中的高维问题，在[24]模型中研究了如何利用降维技术来解决这些问题。

Our work explores the extent to which item-based recommenders, a new class of recommender algorithms, are able to solve these problems.

本文探讨了一类新的推荐算法——基于物品的推荐算法在多大程度上解决了这些问题。

贡献

This paper has three primary research contributions:

本文主要有三个研究贡献:

1. Analysis of the item-based prediction algorithms and identification of different ways to implement its subtasks.

2. Formulation of a precomputed model of item similarity to increase the online scalability of item-based recommendations.

3. An experimental comparison of the quality of several different item-based algorithms to the classic user-based (nearest neighbor) algorithms.

1. 分析了基于项目的预测算法，并确定了实现其子任务的不同方法。
2. 制定一个预先计算的项目相似度模型，以增加基于项目的推荐的在线可扩展性。
3. 实验比较了几种不同的基于项目的算法与经典的基于用户的(最近邻)算法的质量。