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دسته بندی:
سیستم های توصیه گر - recommender systems
سال انتشار:
2019
عنوان انگلیسی مقاله:
GPS: Factorized group preference-based similarity models for sparse sequential recommendation
ترجمه فارسی عنوان مقاله:
GPS: مدلهای شباهت مبتنی بر اولویت گروهی فاکتور شده برای توصیه های پی در پی پراکنده
منبع:
Sciencedirect - Elsevier - Information Sciences, 481 (2019) 394-411: doi:10:1016/j:ins:2018:12:053
نویسنده:
Yeongwook Yang, Danial Hooshyar, Heui Seok Lim
چکیده انگلیسی:
One of the key tasks for recommender systems is the prediction of personalized sequential behavior. There are two primary means of modeling sequential patterns and long-term
user preferences: Markov chains and matrix factorization, respectively. Together, they provide a unified approach to predicting user actions. In spite of their strengths in tackling
dense data, however, these methods struggle with the sparsity issues often present in realworld datasets. In approaching this problem, we propose combining similarity-based methods (demonstrably helpful for sequentially unaware item recommendation) with Markov
chains to offer individualized sequential recommendations. This approach, called GPS (a
factorized group preference-based similarity model), further leverages the idea of group
preference along with user preference to introduce a greater array of interactions between
users—which in turn eases the problem of data sparsity and cold users and cuts down on
the assumption of a strong independency within various factors. By applying our method
to a range of large, real-world datasets, we demonstrate quantitatively that GPS outperforms several state-of-the-art methods, particularly in cases with sparse datasets. Regarding qualitative findings, GPS also grasps personalized interactions and can provide recommendations that are both on-target and meaningful.
Keywords: Recommender systems | Sequential recommendation | Similarity models | Group preference
قیمت: رایگان
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