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Improved Collaborative Filtering Algorithm Using Topic Model

Liu Na, Lu Ying, Tang Xiao-Jun, Wang Hai-Wen, Xiao Peng, Li Ming-Xia
2016 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)  
In this paper, we proposed collaborative filtering algorithm using topic model.  ...  Similarity among users or items is calculated based on rating mostly, without considering explicit properties of users or items involved.  ...  Figure 5 and Figure 7 are compared results with user-based and item-based. It is clear that MAE of our proposed method is lower than baseline.  ... 
doi:10.1109/pdcat.2016.079 dblp:conf/pdcat/LiuLTWXL16 fatcat:lgpylxjv6nbthl7nq3q2mqn25i

Improved Collaborative Filtering Algorithm using Topic Model

Na Liu, Ying Lu, Xiao-Jun Tang, Hai-Wen Wang, Peng Xiao, Ming-Xia Li, T. Gong, T. Yang, J. Xu
2016 ITM Web of Conferences  
In this paper, we proposed collaborative filtering algorithm using topic model.  ...  Similarity among users or items is calculated based on rating mostly, without considering explicit properties of users or items involved.  ...  Figure 5 and Figure 7 are compared results with user-based and item-based. It is clear that MAE of our proposed method is lower than baseline.  ... 
doi:10.1051/itmconf/20160705008 fatcat:afoylljhcnbuze5muszdj36yge

Research on Personalized Recommendation of Educational Resources Based on Big Data

Dewen Seng, Xiuli Chen, Xujian Fang, Xuefeng Zhang, Jing Chen
2018 Educational Sciences: Theory & Practice  
With massive education resources, users are faced with the problem of information overload.  ...  takes the management of educational resources and the big data which form the platform as the background, and designs a personalized recommendation algorithm of educational resources according to the users  ...  The implicit rating method, because of its wide application range, is commonly used. Implicit rating method can be divided into mean value method and collaborative filtering based rating method.  ... 
doi:10.12738/estp.2018.5.094 fatcat:5jzvjycrprc6pnhqx32y46cgpm

A Survey on Hybrid Recommendation Engine for Businesses and Users

Spurthy Mutturaj, Department of ISE, JSS Academy of Technical Education, Bangalore, Karnataka, India, Shwetha B, Sangeetha P, Shivani Beldale, Sahana V
2021 International Journal of Information Engineering and Electronic Business  
In this work we have two objectives 1) Recommend restaurants to user based on user reviews in Yelp dataset and 2) Suggest improvements to business based on user reviews.  ...  In this research, we have analyzed several papers and majority of them have used collaborative and content-based filtering techniques to implement recommender system.  ...  The Traditional recommendation method is based on the collaborative filtering algorithm of the user, and in order to get good results, Amazon suggested a collaborative filtering algorithm.  ... 
doi:10.5815/ijieeb.2021.03.03 fatcat:nzqk7x6w5zf4zen52t4utfsoye

Traveling Route Generation Algorithm Based On LDA and Collaborative Filtering

Peng Cui, Yuming Wang, Chunmei Li
2019 International Journal of Advanced Network, Monitoring, and Controls  
The LDA algorithm based on KDE (Kernel Density Estimation) and classification, the collaborative filtering algorithm based on KDE and classification.  ...  In this work, different recommendation algorithms were designed, including a recommendation algorithm based on Latent Dirichlet Allocation (LDA) and collaborative filtering.  ...  Amazon's G Linden [9] and others proposed an item-based collaborative filtering algorithm, which is well suited for comparing similar items rather than comparing similar users.  ... 
doi:10.21307/ijanmc-2019-021 fatcat:vtaa5bypdfdtpnhdbexsofrl2i

A Probability-Based Hybrid User Model for Recommendation System

Jia Hao, Yan Yan, Guoxin Wang, Lin Gong, Bo Zhao
2016 Mathematical Problems in Engineering  
This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering.  ...  This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.  ...  [21] built a collaborative filter and content-based filter hybrid user model to dynamically recommend Web services. Ronen et al.  ... 
doi:10.1155/2016/9535808 fatcat:6u6d76pw5va5djsc4wtm73ge3e

An Improved Dynamic Collaborative Filtering Algorithm Based on LDA

Meng Di-fei, Liu Na, Li Ming-xia, Su Hao-long
2021 IEEE Access  
This paper proposes an improved dynamic collaborative filtering algorithm named hybrid dynamic collaborative filtering (HDCF), which is based on the topic model.  ...  The calculation of similarity between users is mainly based on ratings, without considering the explicit attributes of users.  ...  Collaborative filtering recommendation algorithms can be divided into two categories: user based collaborative filtering and item based collaborative filtering.  ... 
doi:10.1109/access.2021.3094519 fatcat:6m3iuvqtbjg6bkfbg34inmlmve

A Collaborative Filtering Recommendation Algorithm Improved by Trustworthiness

Shengjun Xie
2014 International Journal of Future Generation Communication and Networking  
Recommender systems based on collaborative filtering have been well studied in both industry and academia fields.  ...  of user-item rating information, based on above two-step nearest neighbor selection, we developed a CF-IF algorithm, which achieves to generate accurate and reliable predicted rating for target user and  ...  Existing recommendation techniques include content based, collaborative filtering, knowledge based and hybrid method.  ... 
doi:10.14257/ijfgcn.2014.7.2.04 fatcat:vltnddz3hvhz5ekqsz5gn7h5gq


Chen Lin, Runquan Xie, Lei Li, Zhenhua Huang, Tao Li
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
In this paper, we investigate the feasibility of integrating content-based methods, collaborative filtering and information diffusion models by employing probabilistic matrix factorization techniques.  ...  Empirical results demonstrate the efficacy and effectiveness of our method, particularly, on handling the socalled cold-start problem.  ...  Based on this assumption, most collaborative filtering methods analyze the click behaviors of news readers instead of the news content, either using a group of users "similar" to the given user to predict  ... 
doi:10.1145/2396761.2398482 dblp:conf/cikm/LinXLHL12 fatcat:lf5fuyax7natljflbq2ydaer6m

Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works

Ayush Singhal, Pradeep Sinha, Rakesh Pant
2017 International Journal of Computer Applications  
We organize the review in three parts: Collaborative system, Content based system and Hybrid system.  ...  Finally, we also provide future directions of research which are possible based on the current state of use of deep learning in recommendation systems.  ...  The performance evaluation is done on ecommerce datasets and compared with several state of art approaches such as BPR-MF, GRU-based method and RNN based methods.  ... 
doi:10.5120/ijca2017916055 fatcat:m6icpquumbgczhrdnya7x35of4

Ontology-based Top-N Recommendations on New Items with Matrix Factorization

Haomin Cui, Ming Zhu, Shijia Yao
2014 Journal of Software  
The mechanism of this method is to find similarities among users in rating score. The item can be recommended based on the similar user's choice.  ...  Collaborative Filter is proved to be effective in recommendations and widely used in the recommender system for online stores.  ...  In this way, the traditional collaborative filtering method could performance better founded on more rating values.  ... 
doi:10.4304/jsw.9.8.2026-2032 fatcat:l76hvstirfakvib5kfucloq7ue

A hybrid approach with collaborative filtering for recommender systems

Gilbert Badaro, Hazem Hajj, Wassim El-Hajj, Lama Nachman
2013 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC)  
The evaluation of the system shows superiority of the solution compared to stand-alone user-based collaborative filtering or item-based collaborative filtering.  ...  We introduce a hybrid approach for solving the problem of finding the ratings of unrated items in a user-item ranking matrix through a weighted combination of user-based and itembased collaborative filtering  ...  In section III, we propose a method that is based on a combination of user-based and item-based collaborative filtering.  ... 
doi:10.1109/iwcmc.2013.6583584 dblp:conf/iwcmc/BadaroHEN13 fatcat:ac5q6mqg5zaetmli2z2zc2qf3i

Neural Collaborative with Sentence BERT for News Recommender System

Budi Juarto, Abba Suganda Girsang
2021 JOIV: International Journal on Informatics Visualization  
The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering.  ...  The evaluation carried out in this study uses precision, recall, and ROC curves to predict news clicks by the user. Another evaluation uses a hit ratio with the leave one out method.  ...  Collaborative filtering uses item ratings, while content-based filtering uses item names and uses the TF-IDF method.  ... 
doi:10.30630/joiv.5.4.678 fatcat:gaboovym3rax3h5js2cod25nxa

Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation

Lianhuan Li, Zheng Zhang, Shaoda Zhang, Yi-Zhang Jiang
2021 Scientific Programming  
This paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative  ...  On the basis of the improved collaborative filtering algorithm, a hybrid algorithm based on content and improved collaborative filtering was proposed.  ...  recommendation module of content filtering. en, based on user interest characteristics, user rating data, and current access sequence data, a recommendation module based on collaborative filtering is  ... 
doi:10.1155/2021/7427409 fatcat:4ovt2gsx2bgazplov6i5m22wbe

Social Collaborative Viewpoint Regression with Explainable Recommendations

Zhaochun Ren, Shangsong Liang, Piji Li, Shuaiqiang Wang, Maarten de Rijke
2017 Proceedings of the Tenth ACM International Conference on Web Search and Data Mining - WSDM '17  
In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations.  ...  Experiments conducted on three large benchmark datasets show the effectiveness of our proposed method for predicting item ratings and for generating explanations.  ...  The two datasets are quite sparse, which may negatively most collaborative filtering methods based on ratings.  ... 
doi:10.1145/3018661.3018686 dblp:conf/wsdm/RenLLWR17 fatcat:57jeaewczbadnbjle6c6n3l6la
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