User based Collaborative Filtering using fuzzy C-means

Measurement 91 (2016) 134–139

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User based Collaborative Filtering using fuzzy C-means Hamidreza Koohi ⇑, Kourosh Kiani Electrical and Computer Engineering Department, Semnan University, Semnan, Iran

a r t i c l e

i n f o

Article history: Received 6 June 2015 Received in revised form 12 May 2016 Accepted 13 May 2016 Available online 14 May 2016 Keywords: Recommender System Collaborative Filtering Clustering K-means Fuzzy C-means Self-organizing map

a b s t r a c t Today, users are surrounded by many items. Recommender Systems are used to help users find items of interest. Collaborative Filtering is one of the most successful techniques of Recommender Systems, which seeks to find users most similar to the active one in order to recommend items. In Collaborative Filtering, clustering techniques can be used for grouping the most similar users into some clusters. Fuzzy Clustering as one of the most frequently used clustering techniques, has not been used in user-based Collaborative Filtering yet. In this paper, a fuzzy C-means approach has been proposed for user-based Collaborative Filtering and its performance against different clustering approaches has been assessed. The MovieLens dataset is used to compare different clustering algorithms. They are evaluated in terms of recommendation accuracy, precision and recall. The empirical results indicate that a combination of Center of Gravity defuzzified Fuzzy Clustering and Pearson correlation coefficient can yield better recommendation results, compared to other techniques. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Recommender Systems (RS) use a large collection of items to predict interesting items for a particular user. The ultimate goal is to recommend items which better match the user’s interests. Items are general concepts that can represent Movies, Books, Music or any consumable things. By aim of e-commerce, so many commercial websites, e.g. Amazon.com, CDnow.com, eBay, Levis, and Moviefinder.com, etc. – have begun to provide recommendation services. Due to the appealing nature of the field, more and more research is being conducted in this regard [1–6]. In general, RS are classified as Collaborative Filtering (CF), Content-based and Hybrid Recommender Systems [7–9]. CF is widely applied in RS, and it is the most successful recommendation technique to date [8,10]. In CF Recommendation, items are predicted by assessing the rating of other users on items. CF Recommendation can be divided into User-Based and Item-Based [7–9]. In general, recommendation can be made via clustering techniques [11–14], in which through clustering in user-based Collaborative Filtering Recommender Systems (CFRS), items given a similar rating by different users are grouped on the basis of the similarity measure of a particular clustering technique. The items in one specific cluster of users are then recommended to the new users whose characteristics are similar to this group. Although ⇑ Corresponding author. E-mail addresses: [email protected] (H. Koohi), [email protected]. ir (K. Kiani). http://dx.doi.org/10.1016/j.measurement.2016.05.058 0263-2241/Ó 2016 Elsevier Ltd. All rights reserved.

Fuzzy C-means is one of the most frequently used clustering techniques, it has not been applied in user-based CF. In this study, the authors have attempted to apply the fuzzy C-means method and compare it to K-means clustering and the self-organizing map neural network in user-based CFRS. In the experiment, two different rate prediction methods are used and for evaluation purposes we carry out real dataset experiments to compare the clustering algorithms. Results show that a combination of Fuzzy Clustering and Pearson correlation achieves the best performance compared to other methods. The remainder of this paper is organized as follows: Section 2 will briefly review previous research studies on RS and the clustering approach; Section 3 introduces the proposed approach in Clustering based CF; Section 4 describes materials and methods and provides experimental results and finally Section 5 outlines conclusions. 2. Literature review RS was introduced in 1992 by Goldberg et al. [15]. Several different approaches have recently been proposed in order to increase the accuracy of the predicted ratings. In the literature, RS is divided into Collaborative Filtering, Content-based and Hybrid Recommender Systems [7–9]. CF techniques use ratings on items to find similarities between users, where recommendation is calculated as a weighted average of similar users’ ratings on target item [16]. This is while Content-based recommendation approaches analyze a set of descriptions of items

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previously rated by a user and then create a profile of user interests based on the features of the items rated by the user [7,8]. On the other hand, Hybrid RS tries to combine the two mentioned approaches to get the advantages of each of them [7,17]. CF algorithms are divided into two categories: Memory-Based CF and Model-Based CF. The Memory-based type makes recommendations based on similarities of the users or items, and produces a prediction for the active user by means of the entire user-item database. It may be further divided into user-based CF and item-based CF [7–9]. In user-based in order to recommend items for the target user, similarities between him and other users are computed based on the assumption that if some users have similar interesting items up to now, they will have similar interests in future. But item-based algorithms look at the similarity between items to make a prediction. The idea is that a user is most likely to purchase items that are similar to the one he already bought in the past. In contrast, Model-based CF approaches use the user-item database to produce a model off-line and operate on reduced data, which helps deal with scalability and sparsity issues [8,18]. Clustering [11,13,14] and dimensionality reduction processes [8,16,19] are popular model-based approaches. Since CF techniques collect users’ preferences about the items and construct personal profiles, they are open to privacy risks so [12] propose privacy preserving CF through the use of some clustering method. Gupta and Tripathy in [20] by combining Content-Based and CFRS methods present a hybrid model that uses the Back propagation Neural Network to train the model. Cheng and Wang in [21]. Based on subjective information that comprised of opinions solicited from domain experts and objective information that clarifies the active user’s preferences based on those of similar users a fuzzy RS was developed that can overcome the weaknesses of traditional CF systems including the sparsity and cold-start problems but this requires users to present their opinions in fuzzy linguistic model. Birtolo et al. in [22] employ Fuzzy Clustering on item-based CFRS and make a trust aware clustering CF. Cluster ensemble techniques in [11] is used for user based CFRS. But we haven’t seen the comparison of Fuzzy Clustering with other clustering techniques on user-based CFRS. In this study, we offer to employ Fuzzy C-means Clustering to improve the recommendation performance compared to other clustering techniques. In particular, we compare performance of user-based CFRS on three famous clustering methods, which are K-means, SOM and fuzzy C-means methods. For the comparison, we use the MovieLens dataset for the experiments, and the evaluation measures are based on the rate of recommendation accuracy, precision and recall.

who have a similar taste to the target user. Table 1 shows an example of rating for movies by users. As mentioned, recommended items to the target user are based on the preferences of the neighbor users [8,9,17]. There are two ways to find the neighbor users including classical similarity measures and clustering algorithms, so similarity measure between users and new user plays an essential role in the rating prediction process. There are several well-known classic similarity measures in the literature [7–9,17,23–26] but empirical analyses show that for user-based CFRS, Pearson correlation coefficient outperforms other measures of comparing users [8,9]. The Pearson similarity between users a and b can be measured by:

P p2P ðr a;p r a Þðr b;p r b Þ ffi ffiqffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi simða; bÞ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P P 2 2 p2P ðr a;p r a Þ p2P ðr b;p r b Þ

ð1Þ

Which P ¼ fp1 ; . . . ; pm g represent the set of items, r i;j is the rate of user i on jth item and ri is the average rating of user i. On the other hand, the aim of clustering algorithms is to group the similar users into some clusters. Moreover, the users who are in the cluster which the target user belongs to are selected as its neighbor users. In this study, we use the clustering method for defining neighbor users as used in [11–14,27,28]. Also, in this regard it was proved that clustering algorithms perform better than the similarity measures for finding the users similar to the target user [28]. Although Pearson similarity is usually used for defining the neighbor users, it can also be used for the rate prediction process [7,8]. In this regard, the prediction for the rating of user a for item p that also factors the relative proximity of the nearest neighbor N is produced by following equation. In real, this prediction is produced as a weighted average of those neighbors having a rating on p:

P

b2N simða; bÞ

P

predða; pÞ ¼ r a þ

ðr b;p r b Þ

b2N simða; bÞ

ð2Þ

However with the help of Pearson correlation we select n/c more similar users in each cluster instead of n users in the cluster. Also another well-known rate prediction method is Maximizing Average Satisfaction [29] that has been shown in Eq. (3), which calculates the average of all ratings of item p from n neighbor users. n X predða; pÞ ¼ 1=n r ip

ð3Þ

i¼1

Finally, the N-top rated items will recommend to the target user. 2.2. Clustering methods

2.1. User based Collaborative Filtering User Based CFRS operates on an n m user–item matrix, where preferences of n users on m items are recorded. When a new user comes demanding recommendation, denoted as the target user, the most similar users as neighbor users in the system are determined. By using their previous ratings on the specific item, a prediction is estimated for the target user on this item. In other words, recommended items are those that were preferred by users

Table 1 An example of user-item matrix.

John Alice Maria

The godfather

Gladiator

Titanic

Star wars

5 – 2

2 4 –

– 3 4

4 – 3

The classical method used to find neighbor users in RS is by using similarity measurement. But they are inadequate for finding the effective similar users particularly in sparse datasets. Another issue is that these measures are computationally complex. To overcome these problems, clustering methods can be utilized in order to group users into different clusters. Clustering can be defined as a process of organizing users in a database into clusters such that users within the same cluster have a high degree of similarity [30]. Therefore, when a user is deemed to be similar to a particular cluster, items which are of interest to that particular group of users are recommended to the user. The use of clustering techniques to identify groups of similar users has been found to significantly boost the performance of collaborating filtering systems due to their sparsity nature. Three well-known and widely used clustering techniques are K-means, SOM and Fuzzy Clustering [11–14,18,27, 30–33].

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3. Research methodology The goal of this paper is to apply fuzzy C-means clustering to user-based CF and show that Fuzzy Clustering outperforms other clustering techniques. The diagram of the system can be seen in Fig. 1. At the first step of the system, dataset is divided to fivefold cross-validation sub sets as used in [11,12]. Each time 80% of dataset employs as training and remaining 20% for testing the recommendation prediction, which in each time of five testing process we will have four training and one testing subsets, that in each test, the training subsets don’t have overlap. Although totally, the testing subsets don’t have any overlap and union of them will be the original rating matrix. As a result, we will have five different results based on the five different testing subsets. We will consider average of these results. In User Clustering process, we select some clustering methods: K-means, SOM and fuzzy C-means, in order to group together users in clusters, minimizing the dissimilarity between elements assigned to the same cluster. Each clustering attempt will run individually with different number of clusters set in order to find the clustering models which can provide the highest rate of recommendation accuracy. Fuzzy C-means is used in order to provide a different membership degree of every user that belongs to different clusters. In order to define what users to consider in the prediction step we apply a defuzzification method. In another word, every user is assigned to k clusters with different degrees of membership, so a defuzzification process is necessary. In this study,

Center of Gravity (COG) and Maximum method of defuzzification techniques are used. Then, the clustering results from each of the clustering techniques can be obtained after the testing subsets are tested. After finding clusters, the neighbor finding step with defining similarity measure between two users elicits the information that correlate users. So the most similar k users are selected as neighbors to contribute to the prediction process. In which, users whose similarity weights satisfy a pre-defined threshold can be chosen as neighbors. While the next step predicts ratings for items similar to rated ones by neighbors. For prediction process we use two wellknown methods; averaging (see Eq. (3)) and using Eq. (2) that uses Pearson correlation coefficient (see Eq. (1)) as a measure of similarity between two users, so the algorithm predicts ratings for items similar to rated ones and selects the Top-N items to recommend.

4. Experimental evaluation 4.1. Dataset In this study, we consider to compare the clustering techniques on various parameters to producing a recommendation list for the target user, using MovieLens dataset (http://grouplens. org/datasets/movielens/). MovieLens dataset which is recognized as the major dataset for evaluating recommendation algorithms was collected by the GroupLens Research group at the University of Minnesota. This dataset contains 100,000 ratings on a scale of 1 (bad film) to 5 (masterpiece) of 1682 movies by 943 users, which each user rates at least 20 items. The Movielens dataset is so sparse; just 6.3% of ratings are available. As mentioned before for evaluating the proposed method, dataset is divided into training and testing parts (80% for training and 20% for testing).

4.2. Evaluation metrics In this paper, recommendation accuracy, precision and recall [8,34] is used for evaluating the performance of the experimented methods (see Eqs. (4)–(6)).

P Accuracy ¼

P True Positive þ True Negative P Total Population

ð4Þ

P

Precision ¼ P

True Positive Test Outcome Positive

ð5Þ

P True Positive Recall ¼ P Realy Positive

ð6Þ

In each experiment, for the active user, a list of items with their corresponding predicted ratings is found as the output of the system. They can be measured by a confusion matrix that shown in Table 2. The accuracy rate is based on selecting high-quality instances from the set of all instances. Precision is the fraction of retrieved instances that are relevant, while recall is the fraction of relevant instances that are retrieved.

Table 2 Confusion matrix. Reality

Test out-come Fig. 1. Experimented model.

Positive Negative

Positive

Negative

True positive False negative (error)

False positive (error) True negative

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4.3. Results

Table 4 Recommendation with SOM.

In the experiment, for each of the predefined three clustering methods i.e. K-means, SOM and fuzzy C-means with different settings, two different types of predictions (Average & Pearson) are examined by their recommendation performance on the mentioned fivefold datasets. Table 3 shows the performances of K-means for different cluster numbers and different prediction methods with regards to the recommendation made. For example when the number of cluster was set to 3, the accuracy of average and Pearson prediction methods were reported 79.46% and 80.1%. The bold value shows the best gained results, as can be seen K-means with 3 clusters especially by Pearson prediction method which outperforms others in terms of accuracy and precision. Furthermore, in another experiment, the SOM neural network was used as another clustering method with different dimensions. The result of performance on mentioned prediction type shown in Table 4. Similar to the other tables, the best results have been shown in bold format. When the dimension was set to 2 ⁄ 3 with using Pearson prediction type the accuracy and precision value were reported 79.73% and 58.29% respectively. Moreover, fuzzy C-means with different setting were examined and as can be seen in Table 5 that shows the performance of fuzzy C-means with maximum defuzzification method, accuracy and precision result of configuration with 3 clusters and Pearson prediction type is better than others. Also Table 6 corresponds the performing of fuzzy C-means with Center of Gravity defuzzification method and different prediction types. As shown in the table, accuracy and precision of 3 clusters by Pearson prediction type is better than others. Table 7 compares different clustering algorithms using the mentioned prediction methods and shows the minimum and maximum value of accuracy. This table shows that Pearson prediction type performs better performance on all recommendation processes. Also, it can be obtained from Tables 3–6 and 7 that performing greater cluster number will lead to lower accuracy and precision; it’s clear from the mentioned tables that three cluster numbers for K-means and C-means and dimension of 2 ⁄ 3 for SOM clustering methods will perform better results on accuracy and precision; Although, lower cluster numbers may increase the computational time [12]. For the rates of recall, the greater accuracy and precision rates do not concluded to higher rate of recall. However, more accurate recommendations to users are more important than a greater number of recommendations. That is, it Table 3 Recommendation with K-means.

Dimension

Prediction type

Accuracy (%)

Precision (%)

Recall (%)

2⁄3

Average Pearson

79.34 79.73

54.29 58.29

12.71 14.66

2⁄4

Average Pearson

79.29 79.64

54.62 57.29

13.67 14.99

2⁄5

Average Pearson

79.16 79.44

52.43 54.81

14.38 15.6

3⁄3

Average Pearson

79.22 79.49

53.15 55.62

14.44 14.97

3⁄4

Average Pearson

79 79.23

51.02 52.58

15.39 16.11

4⁄4

Average Pearson

78.87 79.17

50.1 52.26

15.47 16.4

Average

Average Pearson

79.15 79.45

52.6 55.14

14.34 15.46

Table 5 Recommendation with Fuzzy C-means and max defuzzification method. Cluster no.

Prediction type

Accuracy (%)

Precision (%)

Recall (%)

3

Average Pearson

80.44 81.1

59.76 63.33

10.6 15.54

5

Average Pearson

80.33 80.86

59.1 62.02

13.44 15.1

7

Average Pearson

80.17 80.66

55.1 59.25

14.1 15.96

9

Average Pearson

80.12 80.5

54.26 57

15.68 16.6

11

Average Pearson

79.92 80.3

52.8 55.35

16.24 17.8

13

Average Pearson

79.82 80.08

51 52.74

17.6 17.85

15

Average Pearson

79.6 79.91

49.9 52.3

18 18.44

Average

Average Pearson

80.06 80.49

54.56 57.43

15.1 16.76

Table 6 Recommendation with Fuzzy C-means and COG defuzzification method. Cluster no.

Prediction type

Accuracy (%)

Precision (%)

Recall (%)

3

Average Pearson

79.95 81.41

57.6 64.81

15.4 19.01

5

Average Pearson

79.9 81.38

55.6 64.74

14.4 18.9

7

Average Pearson

79.91 81.28

54.2 63.71

14.6 19.44

Cluster no.

Prediction type

Accuracy (%)

Precision (%)

Recall (%)

3

Average Pearson

79.46 80.1

58.5 62.02

8.35 14.8

9

Average Pearson

79.89 81.33

54.38 64

15.25 19.68

5

Average Pearson

79.42 79.8

56.86 59.5

11.73 14.31

11

Average Pearson

79.86 81.27

53.54 63.47

15.43 19.99

7

Average Pearson

79.33 79.7

54.8 57.83

12.6 14.71

13

Average Pearson

79.9 81.37

54.7 64.36

14.91 19.91

9

Average Pearson

79.31 79.6

54.42 57

13.51 14.36

15

Average Pearson

79.83 81.25

52.14 63.23

15.51 20.35

11

Average Pearson

79.05 79.41

51.72 54.81

14.08 15.34

Average

Average Pearson

79.89 81.33

54.6 64.05

15.07 19.61

13

Average Pearson

79.17 79.34

52.71 53.86

14.66 15.4

15

Average Pearson

79 79.15

51 52.31

14.37 15.41

Average

Average Pearson

79.25 79.6

54.28 56.8

12.76 14.9

is better to recommend movies that users will actually favor rather than to recommend many movies that may possibly contain many less favored movies. Consequently, the accuracy and precision rates should be as high as possible.

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Table 7 Minimum and maximum accuracy of Tables 3–6. Clustering method

Prediction type

Maximum

Minimum

Accuracy (%)

Cluster no.

Accuracy (%)

Cluster no.

K-means

Average Pearson

79.46 80.1

3 3

79 79.15

15 15

SOM

Average Pearson

79.34 79.73

2⁄3 2⁄3

78.87 79.17

4⁄4 4⁄4

C-means & Max

Average Pearson

80.44 81.1

3 3

79.6 79.91

15 15

C-means & COG

Average Pearson

79.95 81.41

3 3

79.83 81.25

15 15

Fig. 2 compares average accuracy of the average prediction type in mentioned clustering method, as can be seen; fuzzy C-means with Maximum defuzzification method outperform other methods in average prediction type. Figs. 3 and 4 show the comparison of average precision and average recall in average prediction type in different clustering method and shows that fuzzy C-means perform better results against K-means and SOM. Comparison of average computed performance in different clustering method by using Pearson prediction type as can be seen in Figs. 5–7 show that fuzzy C-means especially with Center of Gravity defuzzification method performs better results against K-means and SOM in terms of accuracy, precision and recall. Furthermore, Fig. 8 compares average accuracy of two different prediction types in mentioned clustering methods, as can be seen; fuzzy C-means especially with Pearson prediction type and Center of Gravity defuzzification method make better results against other methods. Figs. 9 and 10 show the comparison of average precision and recall in two predefined prediction types by different clustering methods, it can clearly perceive that combining fuzzy C-means and Center of Gravity defuzzification method in Pearson prediction type outperform all other methods.

55 54 53 52 51

Precision

K-means

SOM

C-means & C-means & MAX COG

Fig. 3. Average precision with average prediction type.

16 14 12 10

Recall

K-means

SOM


Fig. 4. Average recall with average prediction type.

Accuracy

82 81 80 79 78

K-means

SOM


Fig. 5. Average accuracy with Pearson prediction type.

5. Conclusion The aim of Recommender Systems is to help users to save time in finding their interests. So finding the best similar users, also called neighbors, and achieving higher recommendation accuracy is a major challenge in these systems. Therefore, this paper addresses this problem by using fuzzy C-means method and comparing its performance against other clustering techniques used in user-based Collaborative Filtering recommendation systems. According to the results achieved on the MovieLens dataset, it can be concluded that the Fuzzy C-means clustering algorithm achieved better results compare to K-means and SOM clustering methods. By using defuzzification methods, Center of Gravity had better performance compare to Maximum approach. Although Pearson correlation prediction type led to better results against Average type in the entire mentioned clustering algorithm, but according to Figs. 8–10, it can be clearly perceived that using

80.5 80 79.5 79 78.5

Accuracy

K-means

SOM

C-means C-means & MAX & COG

Fig. 2. Average accuracy with average prediction type.

Precision

65 60 55 50

K-means

SOM


Fig. 6. Average precision with Pearson prediction type.

25 20 15 10 5 0

Recall

K-means

SOM


Fig. 7. Average recall with Pearson prediction type.


82 81 80 79 78

Accuracy

K-means

SOM

C-means & C-means & MAX COG Average Pearson

Fig. 8. Comparison of average accuracy in two way of prediction.

65 60 55 50 45

Precision

K-means

C-means & C-means & MAX COG Average Pearson SOM

Fig. 9. Comparison of average precision in two way of prediction.

25 20 15 10 5

Recall

K-means

SOM

C-means & C-means & MAX COG Average Pearson

Fig. 10. Comparison of average recall in two way of prediction.

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