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Knowledge Graph Recommendation Model Based on Feature Space Fusion
2022
Applied Sciences
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To solve this problem, this paper presents a new recommendation model named the knowledge graph recommendation model based on feature space fusion (KGRFSF). ...
The existing recommendation model based on a knowledge graph simply integrates the behavior features in a user–item bipartite graph and the content features in a knowledge graph. ...
Last.FM: https://grouplens.org/datasets/hetrec-2011 (all accessed on 28 June 2022).
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app12178764
fatcat:e4tep5d5yzbcppmkj3dmzzp6mm
Fusion Knowledge Graph and Collaborative Filtering Recommendation Algorithm
2020
International Journal of Advanced Trends in Computer Science and Engineering
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the content information of items. ...
The influence of data sparse, the collaborative filtering recommendation algorithm has the problem of inaccurate recommendation. ...
recommends similar items to the user based on the user's historical behavior. ...
doi:10.30534/ijatcse/2020/268952020
fatcat:nsspxzparvabxp3imvhc5jktbi
The Collaborative Filtering Method Based on Social Information Fusion
2019
Mathematical Problems in Engineering
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Experiments show that our method outperforms the existing methods based on user ratings of items and using social information fusion to search similar users is an available way for collaborative filtering ...
Our method first uses social information fusion to search for similar users and then updates the user rating of items for recommendation using similar users. ...
Acknowledgments This work was supported by the National Natural Science Foundation of China (61672040) and the North China University of Technology Startup Fund. ...
doi:10.1155/2019/9387989
fatcat:lsmm6uzdcfcmvok55o5ahhsxbq
Micromedia News Dissemination Impact Assessment Integrated with Personalized Recommendation Algorithm
2021
Advances in Multimedia
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These recommendation algorithms are effective. This promotes the dissemination of news, which also recommends news content that is more relevant to user preferences for most users. ...
The development of the Internet has completely changed the way of recommending and disseminating news content. ...
for news companies to recommend news personalized for users based on the characteristics of users [8] . ...
doi:10.1155/2021/5621864
fatcat:xgvituysk5b67k6d7o75rbwfg4
Implementation of Network Data Mining Algorithm for Associated Users Based on Multi-Information Fusion
2022
Journal of Sensors
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A method is proposed for the implementation of a data mining algorithm of a user network based on the fusion of several data. ...
AUMA-MRL (associated user mining algorithm based on multi-information representation learning) proposes an associated user mining algorithm based on node characteristics, neighborhood information, and ...
Content-based recommendations are predicted directly based on the content information of the item, without requiring relevant evaluation information, but it becomes slightly difficult when the content ...
doi:10.1155/2022/3350997
fatcat:kswsshlgxrhxzm77liaktmxbiy
VideoTopic: Content-Based Video Recommendation Using a Topic Model
2013
2013 IEEE International Symposium on Multimedia
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In this paper, a novel content-based video recommendation framework, called VideoTopic, that utilizes a topic model is proposed. ...
Meanwhile, the visual information of videos is typically not fully explored, which is especially important for recommending new items with limited metadata information. ...
of videos, and links user interests and video content by estimating user interests using the topic distributions of user watched videos. • A new approach is proposed that maps the problem of recommending ...
doi:10.1109/ism.2013.41
dblp:conf/ism/ZhuSW13
fatcat:gheb3npk4nbbrgmkxjklfshwq4
Personalized Paper Recommendation Based on User Historical Behavior
[chapter]
2012
Communications in Computer and Information Science
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The personalized recommendation model is constructed based on contentbased filtering model and statistical language model.. ...
Through collecting the operations on scientific papers of online users and carrying on the detailed analysis, we build preference model for each user. ...
This research is supported by the National Natural Science Foundation of China under Grant No. 61105049. ...
doi:10.1007/978-3-642-34456-5_1
fatcat:wxou46xpmfh4rdxgsxj3qeyfa4
Personalized TV Recommendation: Fusing User Behavior and Preferences
[article]
2020
arXiv
pre-print
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To evaluate the method, we conduct empirical studies on a real-world TV dataset, the results of which demonstrate the superior performance of our model in terms of both recommendation accuracy and time ...
The proposed approach first leverages user viewing patterns regarding time and TV channels to identify potential candidates for recommendation and then further leverages user preferences to rank these ...
Viewing behavior. Here, we define the so-called viewing behavior of users based on the following observations. ...
arXiv:2009.08957v1
fatcat:luhjau64efg6tkiqzrmcukwgne
Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)
2011
Proceedings of the fifth ACM conference on Recommender systems - RecSys '11
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to content attributes, and user consuming behaviors Recommendation of resources annotated in different languages Contextualization of multiple user preferences, e.g. by distinguishing user preferences ...
of short-and long-term user preferences Combination of different types of user preferences: tastes, interests, needs, goals, mood Cross-domain recommendation, based on user preferences about different ...
to content attributes, and user consuming behaviors Recommendation of resources annotated in different languages Contextualization of multiple user preferences, e.g. by distinguishing user preferences ...
doi:10.1145/2043932.2044016
dblp:conf/recsys/CantadorBK11
fatcat:b5qc7nhikncvlbn7iovbspnn4y
The Future of Misinformation Detection: New Perspectives and Trends
[article]
2019
arXiv
pre-print
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We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion ...
Finally, we give our own views on the open issues and future research directions of MID, such as model adaptivity/generality to new events, embracing of novel machine learning models, explanatory detection ...
In [88] , Ruchansky et al. propose a MID model based on RNN, which incorporates features of the news content, the user response, and the source users to promote the performance on fake news detection. ...
arXiv:1909.03654v1
fatcat:34h2os2pzrbm3kqluk5uajtr6i
Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm
2021
Complexity
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The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized ...
method based on weighted classification and user collaborative filtering algorithm. ...
recommending news for users. e core idea of CBR is to recommend the items closest to users' favorite content [21] [22] [23] [24] . ...
doi:10.1155/2021/4476560
fatcat:x2xe6fw6kbfsbo5piulx2hlzg4
College English Precise Teaching Model Using Combination Optimization Collaborative Filtering Algorithm
2022
Mobile Information Systems
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This article develops an English precision teaching platform based on the CF algorithm of combinatorial optimization to accurately recommend English learning materials that meet the learners' personal ...
On different data sets, experimental results show that this method's recommendation accuracy is 94.6%, which is higher than the CF algorithm of multifeature fusion by 4.5% and the traditional CF algorithm ...
New resources will be added to the recommendation system on a regular basis, allowing users to learn new information. ...
doi:10.1155/2022/4504779
fatcat:77vqsybotre7zdou2gouv3p25e
A Novel Recommendation Algorithm Based on Time-aware
2020
Journal of Engineering Science and Technology Review
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To increase novelty based on recommendation accuracy, a novel recommendation algorithm based on time-aware was proposed. ...
The primary key in novel recommendation is to judge whether an item is "new" for target users. The novelty of recommended items to users changes with time. ...
Time-aware content-based recommendation The traditional content-based recommendation (CBR) algorithm assumes that user interests are constant. ...
doi:10.25103/jestr.136.05
fatcat:e72wgoke7ndfrlscqryr2rugrq
Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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Millions of news articles emerge every day. How to provide personalized news recommendations has become a critical task for service providers. ...
We conduct extensive experiments on a commercial news reading dataset, and the results demonstrate that the proposed DFM is superior to several state-of-the-art models. ...
On the other side, content-based filtering (CBF) is a complementary technology to CF that reduces the cold-start problem. ...
doi:10.24963/ijcai.2018/529
dblp:conf/ijcai/LianZXS18
fatcat:bhxe4e7xbzhb3fxqjyjwdszb7m
Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion
2022
Journal of Sensors
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This method recommends key technical problems and solutions based on multi-information fusion to explore the research of user network data mining. ...
In order to explore how to realize network mining for associated users, an algorithm of associated user mining based on recommendation system is proposed. ...
Recommendations based on demographics Content-based recommendations Recommendations based on collaborative filtering Easy to implement Low modeling requirements Advantages Depend on less Grasp users' taste ...
doi:10.1155/2022/2417826
fatcat:pa6iealo3raytizwdbux6euqsi
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