Deep Interest Network Based Book Recommends-A Case Study of College Reader
Authors: 
Kui Zhang, Jianguang Hu, Xing XinAuthors Info & Claims
Kui Zhang, Jianguang Hu, Xing XinAuthors Info & ClaimsPages 84 - 89
Abstract
Data providers widely use Recommendation Systems (RS) to facilitate users getting required knowledge in an information exploration context. Prediction based on item ranking or comments is usually the book recommendation task, in which lots of work has been proposed in a commercial context. However, there are no comments or even ranking data under the college library context. Moreover, the volume of the user dataset is vast and spans years. Different from the recommendation for classic bookselling, the recommendation for the college readers needs to meet their study progress and also follow their reading interesting drift. So, if the existing model can be applied in the library context and how these models performed under the context of the sparse datasets need to be verified. This study applied an interest-oriented ranking algorithm to the book recommendation for college students. Furthermore, a case study was carried out on the BeiHang University library dataset, which consists of 2.32 million user records. An experiment of six groups of different sequence lengths was conducted to validate the ability of DIN in interesting capturing. The result shows that the DIN can perform well in AUC, which on average achieved 76%. Meanwhile, the rapid decrease of the AUC according to the increasing number of sequence lengths also indicates the limitation of long-term interesting capturing.
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Index Terms
- Deep Interest Network Based Book Recommends-A Case Study of College Reader
Recommendations
A novel collaborative filtering algorithm based on social network
ICSI'12: Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part IIWith the explosive growth of the Internet, recommendation systems have been widely used by users. Collaborative Filtering (CF) is one of the most popular approaches for determining recommendations. With the development of Social Network Service(SNS), ...
Development of an offline OOH advertising recommendation system using negative sampling and deep interest network
AbstractThe out-of-home (OOH) advertising market has been operated exclusively following the know-how of salespeople. Thus, it is difficult to make scientific decisions and systematically provide various options to advertisers. In this regard, this study ...
Interest before liking: two-step recommendation approaches
Recommender systems become increasingly significant in solving the information explosion problem. Existing techniques focus on minimizing predicted rating errors and recommend items with high predicted values to people. They consider high and low rating ...
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Published In
December 2023
201 pages
ISBN:9798400709173
DOI:10.1145/3641032
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Publication History
Published: 11 June 2024
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- The Research Funds of Beijing Academic Library
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ICISE 2023
ICISE 2023: 2023 8th International Conference on Information Systems Engineering
December 16 - 18, 2023
Bangkok, Thailand
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