These latter are well known encyclopedic collections of data that can be used to feed a content-based recommender system. In this paper we investigate how the choice of one of the two datasets may influence the performance of a recommendation engine not only in terms of precision of the ...
https://blog.csdn.net/qq_32690999/article/details/77434381 因为开发了一个新闻推荐系统的模块,在推荐算法这一块涉及到了基于内容的推荐算法(Content-Based Recommendation),于是借此机会,基于自己看了网上各种资料后对该分类方法的理解,用尽量清晰明了的语言,结合算法和自己开发推荐模块本身,记录下这些过程,供自己回...
通过将生成的内容片段添加到新用户的历史记录中,历史编码器能够更好地捕捉他们的兴趣,从而提高了两个用户组的性能。 ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models a9faae1c746ed1cef5c5a3610418ce6 ABSTRACT 个性化内容推荐系统已经成为用户在每日新闻网站和图...
Clustering Technology Application in e-Commerce Recommendation System This article analyses two kinds of information filtering technologies used in the e-commerce recommendation system: Content-Based Filtering and Collaborati... JH Wu,Q Liu,SW Luo - IEEE Computer Society 被引量: 18发表: 2008年 Applic...
A ranking based Poisson matrix factorization model for point-of-interest recommendation [J]. Journal of Computer ... 余永红,高阳,王皓 - 《计算机研究与发展》 被引量: 23发表: 2016年 Inferring Polyadic Events With Poisson Tensor Factorization We present a Bayesian factorization model for discovering ...
Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indee
For example, Amazon.com has a popular recommendation service which suggests customized information to each member. Content-based filtering is one of the most extensively used approaches for recommender system. The idea behind content-based filtering is that the given user will probably like the items...
(2017). Content-Based Social Recommendation with Poisson Matrix Factorization. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science(), vol 10534. Springer, ...
We have developed a PubMed article recommendation system, PURE, which is based on content-based filtering. PURE has a web interface by which users can add/delete their preferred articles. Once articles are registered, PURE then performs model-based clustering of the preferred articles and recommend...
we propose a content-based recommendation algorithm based on convolutional neural network (CNN). The CNN can be used to predict the latent factors from the text information of the multimedia resources. To train the CNN, its input and output should first be solved. For its input, the language...