TowardstheNextGenerationofRecommenderSystems资源-CSDN文库

Recommender

Systems

3星 · 超过75%的资源需积分: 10 32 浏览量 2013-08-06 18:13:24 上传评论收藏 234KB PDF 举报

资源推荐

资源详情

资源评论

Towards the Next Generation of Recommender Systems:

A Survey of the State-of-the-Art and Possible Extensions

Gediminas Adomavicius

and Alexander Tuzhilin

Abstract–The paper presents an overview of the field of recommender systems and describes the current

generation of recommendation methods that are usually classified into the following three main

categories: content-based, collaborative, and hybrid recommendation approaches. The paper also

describes various limitations of current recommendation methods and discusses possible extensions that

can improve recommendation capabilities and make recommender systems applicable to an even broader

range of applications. These extensions include, among others, improvement of understanding of users

and items, incorporation of the contextual information into the recommendation process, support for

multi-criteria ratings, and provision of more flexible and less intrusive types of recommendations.

Index Terms–Recommender systems, collaborative filtering, rating estimation methods, extensions to

recommender systems.

1. Introduction

Recommender systems became an important research area since the appearance of the first

papers on collaborative filtering since the mid-1990s [45, 86, 97]. There has been much work

done both in the industry and academia on developing new approaches to recommender systems

over the last decade. The interest in this area still remains high because it constitutes a problem-

rich research area and because of the abundance of practical applications that help users to deal

with information overload and provide personalized recommendations, content and services to

them. Examples of such applications include recommending books, CDs and other products at

Amazon.com [61], movies by MovieLens [67], and news at VERSIFI Technologies (formerly

AdaptiveInfo.com) [14]. Moreover, some of the vendors have incorporated recommendation

capabilities into their commerce servers [78].

However, despite all these advances, the current generation of recommender systems still

requires further improvements to make recommendation methods more effective and applicable

to an even broader range of real-life applications, including recommending vacations, certain

G. Adomavicius is with the Carlson School of Management, University of Minnesota, 321 19

Avenue South,

Minneapolis, MN 55455. Email: gedas@umn.edu.

A. Tuzhilin is with the Stern School of Business, New York University, 44 West 4

Street, New York, NY 10012.

Email: atuzhili@stern.nyu.edu.

types of financial services to investors, and products to purchase in a store made by a “smart”

shopping cart [106]. These improvements include better methods for representing user behavior

and the information about the items to be recommended, more advanced recommendation

modeling methods, incorporation of various contextual information into the recommendation

process, utilization of multi-criteria ratings, development of less intrusive and more flexible

recommendation methods that also rely on the measures that more effectively determine

performance of recommender systems.

In this paper, we describe various ways to extend capabilities of recommender systems.

However, before doing this, we first present a comprehensive survey of the state-of-the-art in

recommender systems in Section 2. Then we identify various limitations of the current

generation of recommendation methods and discuss some initial approaches to extending their

capabilities in Section 3.

2. The Survey of Recommender Systems

Although the roots of recommender systems can be traced back to the extensive work in the

cognitive science [87], approximation theory [81], information retrieval [89], forecasting theories

[6], and also have links to management science [71], and also to the consumer choice modeling

in marketing [60], recommender systems emerged as an independent research area in the mid-

1990’s when researchers started focusing on recommendation problems that explicitly rely on the

ratings structure. In its most common formulation, the recommendation problem is reduced to

the problem of estimating ratings for the items that have not been seen by a user. Intuitively, this

estimation is usually based on the ratings given by this user to other items and on some other

information that will be formally described below. Once we can estimate ratings for the yet

unrated items, we can recommend to the user the item(s) with the highest estimated rating(s).

More formally, the recommendation problem can be formulated as follows. Let C be the

set of all users and let S be the set of all possible items that can be recommended, such as books,

movies, or restaurants. The space S of possible items can be very large, ranging in hundreds of

thousands or even millions of items in some applications, such as recommending books or CDs.

Similarly, the user space can also be very large – millions in some cases. Let u be a utility

function that measures usefulness of item s to user c, i.e.,

:uC S R

→

, where R is a totally

ordered set (e.g., non-negative integers or real numbers within a certain range). Then for each

user

cC∈ , we want to choose such item sS

′

∈

that maximizes the user’s utility. More formally:

, argmax ( , )

cC s ucs

∈

′

∀∈ = (1)

In recommender systems the utility of an item is usually represented by a rating, which indicates

how a particular user liked a particular item, e.g., John Doe gave the movie “Harry Potter” the

rating of 7 (out of 10). However, as indicated earlier, in general utility can be an arbitrary

function, including a profit function. Depending on the application, utility u can either be

specified by the user, as is often done for the user-defined ratings, or is computed by the

application, as can be the case for a profit-based utility function.

Each element of the user space C can be defined with a profile that includes various user

characteristics, such as age, gender, income, marital status, etc. In the simplest case, the profile

can contain only a single (unique) element, such as User ID. Similarly, each element of the item

space S is defined with a set of characteristics. For example, in a movie recommendation

application, where S is a collection of movies, each movie can be represented not only by its ID,

but also by its title, genre, director, year of release, leading actors, etc.

The central problem of recommender systems lies in that utility u is usually not defined

on the whole CS× space, but only on some subset of it. This means u needs to be extrapolated

to the whole space CS× . In recommender systems, utility is typically represented by ratings

and is initially defined only on the items previously rated by the users. For example, in a movie

recommendation application (such as the one at MovieLens.org), users initially rate some subset

of movies that they have already seen. An example of a user-item rating matrix for a movie

recommendation application is presented in Table 1, where ratings are specified on the scale of 1

to 5. The “∅” symbol for some of the ratings in Table 1 means that the users have not rated the

corresponding movies. Therefore, the recommendation engine should be able to estimate

(predict) the ratings of the non-rated movie/user combinations and issue appropriate

recommendations based on these predictions.

K-PAX Life of Brian Memento Notorious

Alice 4 3 2 4

Bob

∅

4 5 5

Cindy 2 2 4

∅

David 3

∅

5 2

Table 1. A fragment of a rating matrix for a movie recommender system.

Extrapolations from known to unknown ratings are usually done by (a) specifying heuristics that

define the utility function and empirically validating its performance, and (b) estimating the

utility function that optimizes certain performance criterion, such as the mean square error.

Once the unknown ratings are estimated, actual recommendations of an item to a user are

made by selecting the highest rating among all the estimated ratings for that user, according to

formula (1). Alternatively, we can recommend N best items to a user or a set of users to an item.

The new ratings of the not-yet-rated items can be estimated in many different ways using

the methods from machine learning, approximation theory and various heuristics. Recommender

systems are usually classified according to their approach to rating estimation, and in the next

section, we will present such a classification that was proposed in the literature and will provide

a survey of different types of recommender systems. The commonly accepted formulation of the

recommendation problem was first stated in [45, 86, 97] and this problem has been studied

extensively since then. Moreover, recommender systems are usually classified into the following

categories, based on how recommendations are made [8]:

• Content-based recommendations: the user is recommended items similar to the ones the

user preferred in the past;

• Collaborative recommendations: the user is recommended items that people with similar

tastes and preferences liked in the past;

• Hybrid approaches: these methods combine collaborative and content-based methods.

In addition to recommender systems that predict the absolute values of ratings that individual

users would give to the yet unseen items (as discussed above), there has been work done on

preference-based filtering, i.e., predicting the relative preferences of users [22, 35, 51, 52]. For

example, in a movie recommendation application preference-based filtering techniques would

focus on predicting the correct relative order of the movies, rather than their individual ratings.

However, this paper focuses primarily on the rating-based recommenders, since it constitutes the

most popular approach to recommender systems.

2.1 Content-based Methods

In content-based recommendation methods, the utility (,)ucs of item s for user c is estimated

based on the utilities ( , )

ucs assigned by user c to items

∈

that are “similar” to item s. For

example, in a movie recommendation application, in order to recommend movies to user c, the

content-based recommender system tries to understand the commonalities among the movies

user c has rated highly in the past (specific actors, directors, genres, subject matter, etc.). Then,

only the movies that have a high degree of similarity to whatever user’s preferences are would

get recommended.

The content-based approach to recommendation has its roots in information retrieval [7,

89] and information filtering [10] research. Because of the significant and early advancements

made by the information retrieval and filtering communities and because of the importance of

剩余42页未读，继续阅读

评论收藏

内容反馈

oneinmore

2014-06-10

对发展趋势很有见地

技术笔记本

粉丝: 121
资源: 24

Towards the Next Generation of Recommender Systems

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

[Wireless Networks and Mobile Communications-07]The Internet of Things: From RFID to the Next-Generation Pervasive Networked Systems

Audio Signal Processing for Next-Generation Multimedia Communication Systems

下一代无线系统与网络 Next Generation Wireless Systems and Networks

推荐系统的循序进阶读物

推荐系统的循序进阶读物（从入门到精通）

Towards the Next Video Standard: High Efficiency Video Coding

Long Term Evolution: Towards 4th Generation of Mobile Telephony and Beyond

Towards Next-Generation Botnets

TOWARDS THE HARMONIZATION OF SAFETY ASSESSMENT METHODS OF AUTOMA

Building Recommendation Engines

Building Recommendation Engines.epub

Android代码-TagRec

Softimage Towards a New Theory of the Digital Image 无水印pdf

Towards Robust Distributed Systems.pdf

Web Data Mining and the Development of Knowledge-Based Decision Systems2017

美国光纤通讯展览会及研讨会(OFC)2015年全球光通信大会论文集分卷2

Rolling Out 5G

Deep Learning based Recommender System: A Survey and New Perspectives

Wheeled Mobile Robotics. From Fundamentals Towards Autonomous Systems

Towards Robust Distributed Systems

Wheeled Mobile Robotics From Fundamentals Towards Autonomous Systems

Java 面经手册·小傅哥.pdf

解压后拖入浏览器扩展程序使用.zip

103套PPT模板.zip

Beyond Compare 免安装直接使用

notepad++.exe官网下载

最新资源