In recent years, the recommender system has been widely used in online platforms, which can extract useful information from giant volumes of data and recommend suitable items to the user according to user preferences. In this article, we put forward a crossdomain recommendation method based on the rating data of the different projects from similar users, introducing project cluster effect in the target domain of the study, the use of this specific group of singular value decomposition with the method of extracting information associated with a project with similar characteristics. This method could effectively solve the problem of data sparsity. Due to the sparsity of the target domain, most items in the test set of the target domain have few scores, and their information is challenging to obtain from the training set. A strictly related problem is the one of collaborative filtering in recommender systems, where an algorithm tries to extrapolate missing information about the items from the rating activity of the users in order to provide a specific ad-hoc ranking for each user also on the items that have not been rated (on this see discuss how to aggregate the information from multilayer networks, while showing the importance of centrality measures for this issue). MovieLens data analysis indicated that, compared with the existing recommendation methods and cross-domain recommendation methods, the proposed new method of cross-domain recommendation with cluster effect has a significant improvement in the prediction accuracy.
ZHAI Haoran
,
ZHANG Sanguo
. A new cross-domain recommendation method with cluster effect[J]. Journal of University of Chinese Academy of Sciences, 2025
, 42(2)
: 153
-158
.
DOI: 10.7523/j.ucas.2023.040
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