jmlr jmlr2012 jmlr2012-88 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Joonseok Lee, Mingxuan Sun, Guy Lebanon
Abstract: Recommendation systems are important business applications with significant economic impact. In recent years, a large number of algorithms have been proposed for recommendation systems. In this paper, we describe an open-source toolkit implementing many recommendation algorithms as well as popular evaluation metrics. In contrast to other packages, our toolkit implements recent state-of-the-art algorithms as well as most classic algorithms. Keywords: recommender systems, collaborative filtering, evaluation metrics
Reference: text
sentIndex sentText sentNum sentScore
1 EDU College of Computing Georgia Institute of Technology Atlanta, Georgia 30332, USA Editor: Soeren Sonnenburg Abstract Recommendation systems are important business applications with significant economic impact. [sent-5, score-0.038]
2 In recent years, a large number of algorithms have been proposed for recommendation systems. [sent-6, score-0.489]
3 In this paper, we describe an open-source toolkit implementing many recommendation algorithms as well as popular evaluation metrics. [sent-7, score-0.797]
4 In contrast to other packages, our toolkit implements recent state-of-the-art algorithms as well as most classic algorithms. [sent-8, score-0.26]
5 Keywords: recommender systems, collaborative filtering, evaluation metrics 1. [sent-9, score-0.411]
6 Introduction As the demand for personalized services in E-commerce increases, recommendation systems are emerging as an important business application. [sent-10, score-0.632]
7 com, for example, provides personalized product recommendations based on previous purchases. [sent-12, score-0.184]
8 A wide variety of algorithms have been proposed by the research community for recommendation systems. [sent-17, score-0.489]
9 Unlike classification where comprehensive packages are available, existing recommendation systems toolkits lag behind. [sent-18, score-0.763]
10 They concentrate on implementing traditional algorithms rather than the rapidly evolving state-of-the-art. [sent-19, score-0.126]
11 Implementations of modern algorithms are scattered over different sources which makes it hard to have a fair and comprehensive comparison. [sent-20, score-0.108]
12 In this paper we describe a new toolkit PREA (Personalized Recommendation Algorithms Toolkit), implementing a wide variety of recommendation systems algorithms. [sent-21, score-0.73]
13 PREA offers implementation of modern state-of-the-art recommendation algorithms as well as most traditional ones. [sent-22, score-0.563]
14 In addition, it provides many popular evaluation methods and data sets. [sent-23, score-0.067]
15 As a result, it can be used to conduct fair and comprehensive comparisons between different recommendation algorithms. [sent-24, score-0.573]
16 The implemented evaluation routines, data sets, and the open-source nature of PREA makes it easy for third parties to contribute additional implementations. [sent-25, score-0.067]
17 Not surprisingly, the performance of recommendation algorithms depends on the data characteristics. [sent-26, score-0.489]
18 , 2012a,b) For example, some algorithms may work better or worse depending on the amount of missing data (sparsity), distribution of ratings, and the number of users and items. [sent-28, score-0.029]
19 Furthermore, the different evaluation methods that have been proposed in the literature may have conflicting orderings over algorithms (Gunawardana and Shani, 2009). [sent-30, score-0.116]
20 L EE , S UN AND L EBANON algorithms using a variety of evaluation metrics may clarify which algorithms perform better under what circumstance. [sent-32, score-0.183]
21 The toolkit consists of three groups of classes, as shown in Figure 1. [sent-37, score-0.189]
22 The top-level routines of the toolkit may be directly called from other programming environments like Matlab. [sent-38, score-0.229]
23 The top two groups implement basic data structures and recommendation algorithms. [sent-39, score-0.489]
24 The third group (bottom) implements evaluation metrics, statistical distributions, and other utility functions. [sent-40, score-0.138]
25 1 Input Data File Format As in the WEKA1 toolkit PREA accepts the data in ARFF2 (Attribute-Relation File Format). [sent-42, score-0.189]
26 Since virtually all recommendation systems data sets are sparse, PREA implements sparse (rather than dense) ARFF format. [sent-43, score-0.588]
27 2 Evaluation Framework For ease of comparison, PREA provides the following unified interface for running the different recommendation algorithms. [sent-45, score-0.489]
28 • • • • Instantiate a class instance according to the type of the recommendation algorithm. [sent-46, score-0.489]
29 Given a test set, predict user ratings over unseen items. [sent-48, score-0.215]
30 Given the above prediction and held-out ground truth ratings, evaluate the prediction using various evaluation metrics. [sent-49, score-0.067]
31 Note that lazy learning algorithms like the constant models or memory-based algorithms may skip the second step. [sent-50, score-0.023]
32 A simple train and test split is constructed by choosing a certain proportion of all ratings as test set and assigning the remaining ratings to the train set. [sent-51, score-0.318]
33 3 Data Structures PREA uses a sparse matrix representation for the rating matrices which are generally extremely sparse (users provide ratings for only a small subset of items). [sent-67, score-0.269]
34 Figure 2 (left) shows an example of a sparse vector, containing data only in indices 1, 3, and 6. [sent-70, score-0.028]
35 This design is also useful for fast transposing of sparse matrices by interchanging rows and columns. [sent-73, score-0.055]
36 Figure 2 (right) shows an example of sparse matrix. [sent-74, score-0.028]
37 Figure 2: Sparse Vector (left) and Matrix (right) Implementation PREA also uses dense matrices in some cases. [sent-75, score-0.038]
38 For example, dense representations are used for low-rank matrix factorizations or other algebraic operations that do not maintain sparsity. [sent-76, score-0.089]
39 The dense representations are based on the matrix implementations in the Universal Java Matrix Package (UJMP) (http://www. [sent-77, score-0.059]
40 4 Implemented Algorithms and Evaluation Metrics PREA implements the following prediction algorithms: • Baselines (constant, random, overall average, user average, item average): make little use of personalized information for recommending items. [sent-81, score-0.266]
41 • Memory-based Neighborhood algorithms (user-based, item-based collaborative filtering and their extensions including default vote, inverse user frequency): predict ratings of unseen items by referring those of similar users or items. [sent-82, score-0.478]
42 • Matrix Factorization methods (SVD, NMF, PMF, Bayesian PMF, Non-linear PMF): build low-rank user/item profiles by factorizing training data set with linear algebraic techniques. [sent-83, score-0.054]
43 • Others: recent state-of-the-art algorithms such as Fast NPCA and Rank-based collaborative filtering. [sent-84, score-0.183]
44 We provide popular evaluation metrics as follows: • Accuracy for rating predictions (RMSE, MAE, NMAE): measure how much the predictions are similar to the actual ratings. [sent-85, score-0.216]
45 • Rank-based evaluation metrics (HLU, NDCG, Kendall’s Tau, and Spearman): score depending on similarity between orderings of predicted ratings and those of ground truth. [sent-86, score-0.391]
46 Related Work Several other open-source recommendation toolkits are available. [sent-88, score-0.66]
47 Table 1 summarizes the implemented features in these toolkits and compares them to those in PREA. [sent-89, score-0.171]
48 It also supports powerful mathematical and statistical operations as it is a general-purpose machine learning toolkit. [sent-91, score-0.022]
49 Cofi6 implements several traditional algorithms with a simple design based on providing wrappers for publicly available data sets. [sent-93, score-0.121]
50 MyMedia7 is a C#-based recommendation toolkit which supports most traditional algorithms and several evaluation metrics. [sent-94, score-0.817]
51 As indicated in Table 1, existing toolkits widely provide simple memory-based algorithms, while recent state-of-the-art algorithms are often not supported. [sent-95, score-0.171]
52 Also, these toolkits are generally limited in their evaluation metrics (with the notable exception of MyMedia). [sent-96, score-0.354]
53 In contrast, PREA provides a wide coverage of the the most up-to-date algorithms as well as various evaluation metrics, facilitating a comprehensive comparison between state-of-the-art and newly proposed algorithms in the research community. [sent-97, score-0.137]
54 The open-source nature of the software (available under GPL) may encourage other recommendation systems experts to add their own algorithms to PREA. [sent-104, score-0.489]
55 More documentation for developers as well as user tutorials are available on the web (http://prea. [sent-105, score-0.084]
56 A survey of accuracy evaluation metrics of recommendation tasks. [sent-140, score-0.672]
57 Bayesian probabilistic matrix factorization using markov chain monte carlo. [sent-185, score-0.077]
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