A recommendation model for personalized book lists

Maneewongvatana, Suthathip; Maneewongvatana, Songrit

doi:10.1109/iscit.2010.5664873

Cited by 14 publications

(3 citation statements)

References 5 publications

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“…This method is used in the model built by liu et al [19], which proves that the blending neural network outperforms the blending with Linear Regression. Maneewongvatana et al [20] used k-means clustering to recommend books in university libraries. They browsed the library's borrowing history, and after cleaning the data, they assigned them to different clusters based on the similarity of the topics.…”

Section: Book Recommendation Methodsmentioning

confidence: 99%

Exploring Clustering-Based Reinforcement Learning for Personalized Book Recommendation in Digital Library

Wang

Guo

et al. 2021

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Digital library as one of the most important ways in helping students acquire professional knowledge and improve their professional level has gained great attention in recent years. However, its large collection (especially the book resources) hinders students from finding the resources that they are interested in. To overcome this challenge, many researchers have already turned to recommendation algorithms. Compared with traditional recommendation tasks, in the digital library, there are two challenges in book recommendation problems. The first is that users may borrow books that they are not interested in (i.e., noisy borrowing behaviours), such as borrowing books for classmates. The second is that the number of books in a digital library is usually very large, which means one student can only borrow a small set of books in history (i.e., data sparsity issue). As the noisy interactions in students’ borrowing sequences may harm the recommendation performance of a book recommender, we focus on refining recommendations via filtering out data noises. Moreover, due to the the lack of direct supervision information, we treat noise filtering in sequences as a decision-making process and innovatively introduce a reinforcement learning method as our recommendation framework. Furthermore, to overcome the sparsity issue of students’ borrowing behaviours, a clustering-based reinforcement learning algorithm is further developed. Experimental results on two real-world datasets demonstrate the superiority of our proposed method compared with several state-of-the-art recommendation methods.

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Section: Book Recommendation Methodsmentioning

confidence: 99%

Exploring Clustering-Based Reinforcement Learning for Personalized Book Recommendation in Digital Library

Wang

Guo

et al. 2021

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“…The personalized recommendation also needs extra space to store the users’ preferences. Since, most of the existing book recommendation systems are designed using collaborative filtering and content‐based filtering, they use customer's preferences that is designed for personal users and not for the curriculum of university students. Hence, we cannot treat them as a standard for recommending books to university students for their studies.…”

Section: Introductionmentioning

confidence: 99%

An OWA-Based Ranking Approach for University Books Recommendation

Sohail

Siddiqui

Ali

2017

Int. J. Intell. Syst.

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Generally the book recommendation approaches are personalized in nature, that is, they utilize the users’ purchasing behavior to recommend them the book similar to their preferences. The main problem with the personalized recommendation is its knowledge requirement about users’ past preferences. As a result, these techniques fail in producing appropriate recommendation for a new user whose preferences are not known. The personalized recommendation also needs extra space to store the users’ preferences. In this paper, a framework to recommend books to university students for their studies is presented. In order to answer which books are to be included in the syllabus, a specialized way of recommendation, where recommendations from experts of the subjects at different universities are considered, is presented. We have suggested a ranked recommendation approach for books, which employ Ordered Weighted Aggregation (OWA), a fuzzy‐based aggregation, to aggregate the several ranking of the top universities. On the one hand, it does not need user prior preferences, and on the other hand, it eases the complexities of personalized recommendation to huge number of users and replaces it with a single ranked recommendation. The experimental results are compared with the existing positional aggregation algorithm that demonstrates significant improvement in the results with respect to various performance metrics.

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“…Numerous academic libraries have begun to provide personalized services, such as book recommendations, to attract readers to use library resources [1,2,3,4].…”

Section: Introductionmentioning

confidence: 99%

Community Detection Based on Readers' Borrowing Records

Liu¹,

Haihong²,

Song³

2013

2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical A

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Academic libraries have recently adopted recommender systems to provide personalized service for increased library-resource use and personalized educations. Readers' borrowing records help libraries to realize reader preferences and the recommender systems further provide book recommendations for readers. To apply the readers' borrowing records in recommendations, we give some inceptions in this paper. We find out that most of the people only borrow one or two books once a time and the interval between two successive borrowing records is usually shot as half a month. And by constructing the reader-reader similarity network, we find it have some characteristics: scale-free distribution, the small-world effect and strong community structure. And we propose three different algorithms to detect the communities in the reader-reader similarity network. At last, we compare the proposed algorithms to the existing one on the real world dataset.

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A recommendation model for personalized book lists

Cited by 14 publications

References 5 publications

Exploring Clustering-Based Reinforcement Learning for Personalized Book Recommendation in Digital Library

Exploring Clustering-Based Reinforcement Learning for Personalized Book Recommendation in Digital Library

An OWA-Based Ranking Approach for University Books Recommendation

Community Detection Based on Readers' Borrowing Records

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