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The error comparison is performed using the Root mean square error using five different categories of movies and the result shows that the proposed system gives lesser error rate

CD-SPM: Cross-domain book recommendation using sequential pattern mining and rule mining

Journal of King Saud University - Computer and Information Sciences, (2019)

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摘要

Abstract Recommender system suggests a personalized recommendation by filtering the information based on users interest. Nowadays, users like to purchase the best possible items and services to spend the shortest span of time. The cross-domain recommendation system is a method of recommendation wherein knowledge is gathered from multipl...更多

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简介
  • Recommender system (RS) is a subclass of information filtering system.
  • It helps in finding the user interested items from a huge amount of items.
  • The exponential growth of the internet and the explosion of online data become an information overhead problem.
  • RS is used in various fields for reducing information overhead problems like e-commerce, e-learning, movies, music, news, books and research articles (Zhou et al, 2018; Abdullah et al, 2019).
  • The authors can increase the average order value and reduce the traffic in services and improve the delivery of relevant content to the user (Cambria et al, 2017; Al-Adrousy et al, 2015)
重点内容
  • Recommender system (RS) is a subclass of information filtering system
  • RS is used in various fields for reducing information overhead problems like e-commerce, e-learning, movies, music, news, books and research articles (Zhou et al, 2018; Abdullah et al, 2019)
  • The experimental results show that the proposed model achieves 57% more precision improvement compared with spanning graphs and kNN approach
  • We have found that the CD-Sequential Pattern Mining (SPM) gives higher F1 Score measure, which specifies the recommendation accuracy
  • Wpath helps to find the semantic similarity of items belonging to multiple domains
  • The error comparison is performed using the Root mean square error (RMSE) using five different categories of movies and the result shows that the proposed system gives lesser error rate
方法
  • Dataset description the given movie are found using Wpath.
  • CF is applied on itemrating matrix.
  • By using this item-rating matrix, the item-item similarity matrix is found.
  • To find the item-item similarity, Adjusted.
  • Cosine similarity measure is applied.
  • The Adjusted Cosine similarity between two item vectors (i,j) can be calculated using Eq (2)
结果
  • Analysis of error rate.
  • The authors' experiment result shows that User-based CF Root Mean Square Error (RMSE) is 2.95 in the case of movie Get shortly and 2.530 with Toy Story.
  • In Item-based CF, RMSE rate is 3.37 in the case of movie Get Shortly and 2.96 with movie Twelve Monkeys.
  • When the authors talk about Userbased CF, Mean Square Error (MSE) is 3.50 in the case of movie Get Shortly and 3.06 with the movie Sabrina.
  • Support v/s number of sequential patterns and total time required
结论
  • The proposed approach CD-SPM can recommend the most preferred items with better recommendation accuracy from different domains by combining Wpath, Collaborative Filtering and SPM.
  • Wpath helps to find the semantic similarity of items belonging to multiple domains.
  • PrefixSpan algorithm helps to retrieve the frequent sequences and Topseq rules fetch the preferred items in a sequence.
  • The error comparison is performed using the RMSE using five different categories of movies and the result shows that the proposed system gives lesser error rate.
  • The proposed approach alleviates the new user problem and sparsity problem to some extent as the knowledge of one domain is applied in another domain.The proposed work provides diversified recommendation with respect to the two domains considered
总结
  • Introduction:

    Recommender system (RS) is a subclass of information filtering system.
  • It helps in finding the user interested items from a huge amount of items.
  • The exponential growth of the internet and the explosion of online data become an information overhead problem.
  • RS is used in various fields for reducing information overhead problems like e-commerce, e-learning, movies, music, news, books and research articles (Zhou et al, 2018; Abdullah et al, 2019).
  • The authors can increase the average order value and reduce the traffic in services and improve the delivery of relevant content to the user (Cambria et al, 2017; Al-Adrousy et al, 2015)
  • Methods:

    Dataset description the given movie are found using Wpath.
  • CF is applied on itemrating matrix.
  • By using this item-rating matrix, the item-item similarity matrix is found.
  • To find the item-item similarity, Adjusted.
  • Cosine similarity measure is applied.
  • The Adjusted Cosine similarity between two item vectors (i,j) can be calculated using Eq (2)
  • Results:

    Analysis of error rate.
  • The authors' experiment result shows that User-based CF Root Mean Square Error (RMSE) is 2.95 in the case of movie Get shortly and 2.530 with Toy Story.
  • In Item-based CF, RMSE rate is 3.37 in the case of movie Get Shortly and 2.96 with movie Twelve Monkeys.
  • When the authors talk about Userbased CF, Mean Square Error (MSE) is 3.50 in the case of movie Get Shortly and 3.06 with the movie Sabrina.
  • Support v/s number of sequential patterns and total time required
  • Conclusion:

    The proposed approach CD-SPM can recommend the most preferred items with better recommendation accuracy from different domains by combining Wpath, Collaborative Filtering and SPM.
  • Wpath helps to find the semantic similarity of items belonging to multiple domains.
  • PrefixSpan algorithm helps to retrieve the frequent sequences and Topseq rules fetch the preferred items in a sequence.
  • The error comparison is performed using the RMSE using five different categories of movies and the result shows that the proposed system gives lesser error rate.
  • The proposed approach alleviates the new user problem and sparsity problem to some extent as the knowledge of one domain is applied in another domain.The proposed work provides diversified recommendation with respect to the two domains considered
表格
  • Table1: The Illustration of Semantic Similarity Methods on Some Concept Pair (Movie-Book) Examples
  • Table2: Comparison of error rates
  • Table3: Support vs Number of Sequential patterns and Total time required
  • Table4: Confidence v/s No of Sequential patterns and Total time required
  • Table5: Comparison of Precision, Recall and F1 score
Download tables as Excel
基金
  • The experimental results show that the proposed model achieves 57% more precision improvement compared with spanning graphs and kNN approach
研究对象与分析
real-world datasets with three domains: 5
Zhang et al (2017) proposed a CDRS using consistent information transfer which maintains the consistency during the transferring of knowledge from one domain to other domain. For experimental testing, five real-world datasets with three domains, i.e., books, movies and music were used. The result shows that consistent information transfer increases the accuracy of recommendations in the target domain

users: 943
It helps to mine the sequences and retrieve the most preferred sequence of items (Pei et al, 2001; Ma and Ye, 2018). We have used the freely available dataset for Movie domain from MovieLens 100 K dataset which contains 100,000 ratings of 1682 movies given by 943 users.1. The details (User id, Movie id, Movie name, rating, genre) are obtained

monkeys: 12
Confidence v/s number of sequential rules By considering different category of movies the graph is plotted between confidence and number of sequential rules generated and. Toy story Get shortly Twelve monkeys Sabrina City of lost children. No of sequential patterns

monkeys: 12
Uma, CD-SPM: Cross-domain book recommendation using sequential pattern mining and rule mining, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.01.012. Toy Story Get shortly Twelve monkeys Sabrina City of lost children be transferred to another domain. Furthermore, contextual recommendations can be given considering changing users interests with time and measure the impact of it on recommendation results

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作者
Taushif Anwar
Taushif Anwar
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