[논문]Movie Recommendation System Based on Users' Personal Information and Movies Rated Using the Method of k-Clique and Normalized Discounted Cumulative Gain

Vilakone, Phonexay; Xinchang, Khamphaphone; Park, Doo-Soon

doi:10.3745/jips.04.0169

Movie Recommendation System Based on Users' Personal Information and Movies Rated Using the Method of k-Clique and Normalized Discounted Cumulative Gain 원문보기

Journal of information processing systems, v.16 no.2, 2020년, pp.494 - 507

Vilakone, Phonexay (Dept. of Computer Sciences and Engineering, Soonchunhyang University) , Xinchang, Khamphaphone (Dept. of Computer Sciences and Engineering, Soonchunhyang University) , Park, Doo-Soon (Dept. of Computer Software Engineering, Soonchunhyang University)

Abstract ▼ AI-Helper

This study proposed the movie recommendation system based on the user's personal information and movies rated using the method of k-clique and normalized discounted cumulative gain. The main idea is to solve the problem of cold-start and to increase the accuracy in the recommendation system further instead of using the basic technique that is commonly based on the behavior information of the users or based on the best-selling product. The personal information of the users and their relationship in the social network will divide into the various community with the help of the k-clique method. Later, the ranking measure method that is widely used in the searching engine will be used to check the top ranking movie and then recommend it to the new users. We strongly believe that this idea will prove to be significant and meaningful in predicting demand for new users. Ultimately, the result of the experiment in this paper serves as a guarantee that the proposed method offers substantial finding in raw data sets by increasing accuracy to 87.28% compared to the three most successful methods used in this experiment, and that it can solve the problem of cold-start.

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표/그림 (16)

표 Table 1. Comparison of similar works
그림 Fig. 1. Example of a graph with k-clique.
그림 Fig. 2. Workflow of the proposed method.
표 Table 2. Movie IDs and their ratings
표 Table 3. Movie1 and its rating
표 Table 4. Table for calculating the value of DCG
표 Table 5. DCG result
표 Table 6. Movie1 and its rating after sorting the values from max to min and the selected movie ID and its rating
표 Table 7. IDCG calculation
표 Table 8. IDCG result
표 Table 9. NDCG result
표 Table 10. Recommended movies for the new users
그림 Fig. 3. MAPE result.
표 Table 11. Result value of MAPE for the proposed method
그림 Fig. 4. Comparison of MAPEs of the existing method.
표 Table 12. MAPE result of the CF-kNN+NDCG method

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제안 방법

(2) To assess the performance, the proposed approach is compared to the three most successful methods used in the experiment in this study: the approach of the k-clique techniques, the approach of collaborative filtering using the k nearest neighbor combined with the normalized discounted cumulative gain method (CF-kNN+NDCG), and the k-clique with the association rule mining to evaluate the result of the experiment. The process of experimenting with the k-clique methods and k-clique with the association rule mining method is similar to the operation of the proposed method, but there is a difference in the process of listing the recommended movie to the new user.
After the experiment was completed, to guarantee that the proposed method is the best method that offers higher accuracy than some of the methods used in this experiment, the testing dataset was used as a tool to evaluate the accuracy of the result from the proposed method. For the calculation, the mean absolute percentage error (MAPE) equation is used to calculate the value of the accuracy [23-25].
In this process, various groups of users will appear. Later, the new users will check for a suitable group to join by using the approach of the cosine measure to measure the personal information of the new users with the personal data of the users in each group. In some cases, a new user can belong in more than one group, which depends on the value of the similarity measuring the result.
● Normalized discounted cumulative gain method: This technique is an evolution ranking measure used in the Web search engine algorithms [20,21]. The concept of this technique involves using a relevant scale of the score in the document at a search engine output set after examining the gain of a document based on its position in the output table. There are two advantages of this ranking measure: first is that it allows any retrieved document that has scored relevance; the second advantage involves a discount function over the rank.
, it is tough to recommend something to the new user who has not yet stored his/her information in the system in advance. Therefore, this study proposed a method that helps solve the cold-start problem and realize higher satisfaction than the existing method used in the recommendation system. The idea of this proposed method used the personal information of the users to classify users into several communities with the help of the k-clique, which is a social network analysis method.
Therefore, to increase the accuracy further compared to the recent result in the recommendation system, avoid the problem of cold-start, and create a new technique instead of using the standard technology in the recommendation system, the movie recommendation system based on the user’s personal information and movies rated using the method of k-clique and normalized discounted cumulative gain will be examined in this paper.

이론/모형

Some results of each step from the experiment in this paper will be presented in this chapter. According to the brief description in step 7 from Fig. 2, to generate the recommended movies, the normalized discounted cumulative gain method was used to calculate the famous movie. For the details of how to calculate the ranking, the measure is shown below.
The idea of this proposed method used the personal information of the users to classify users into several communities with the help of the k-clique, which is a social network analysis method. After that, the system will generate the recommended movies for the new users from the list of the movies in the best suitable community and provide them to the new user by using the normalized discounted cumulative gain method. The result value for MAPE of the existing method used in this paper as shown in Fig.
After the experiment was completed, to guarantee that the proposed method is the best method that offers higher accuracy than some of the methods used in this experiment, the testing dataset was used as a tool to evaluate the accuracy of the result from the proposed method. For the calculation, the mean absolute percentage error (MAPE) equation is used to calculate the value of the accuracy [23-25]. MAPE is widely used in the field of statistics structure for predicting the accuracy of the predictive method; the equation of MAPE is shown below.
(1) We proposed a movie recommendation system based on the users’ personal information and movies rated using the method of k-clique and normalized discounted cumulative gain. In the proposed approach, the personal data of the users are used for measuring the similarity among them by using the method of cosine similarity measure. After measuring the similarity among the users and completing the result, it will be converted into a relationship table, which in turn will be converted into a network graph later.
Therefore, this study proposed a method that helps solve the cold-start problem and realize higher satisfaction than the existing method used in the recommendation system. The idea of this proposed method used the personal information of the users to classify users into several communities with the help of the k-clique, which is a social network analysis method. After that, the system will generate the recommended movies for the new users from the list of the movies in the best suitable community and provide them to the new user by using the normalized discounted cumulative gain method.

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