Efficient and Effective Implicit-Feedback-Based Content-Aware Collaborative Filtering For Location Recommendation

2019 ◽  
Vol 7 (2) ◽  
pp. 644-648
Author(s):  
B. SaiSrilekha ◽  
K.S. Yuvaraj
Keyword(s):  
Collaborative Filtering ◽  
Implicit Feedback ◽  
Location Recommendation ◽  
Content Aware

Scalable Content-Aware Collaborative Filtering for Location Recommendation

2018 ◽  
Vol 30 (6) ◽  
pp. 1122-1135 ◽  
Author(s):  
Defu Lian ◽  
Yong Ge ◽  
Fuzheng Zhang ◽  
Nicholas Jing Yuan ◽  
Xing Xie ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Location Recommendation ◽  
Content Aware

scholarly journals Enhanced Collaborative Filtering for Personalized E-Government Recommendation

2021 ◽  
Vol 11 (24) ◽  
pp. 12119
Author(s):  
Ninghua Sun ◽  
Tao Chen ◽  
Wenshan Guo ◽  
Longya Ran
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Information Overload ◽  
State Of The Art ◽  
Interaction Matrix ◽  
Implicit Feedback ◽  
Item Information ◽  
Negative Item ◽  
Latent Features ◽  
Government Websites

The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and users by the latent features inferred from the user–item interaction matrix. A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback. This problem negatively affected the performance of the recommendation system in e-government. In this paper, we firstly propose to learn positive items’ latent features by leveraging both the negative item information and the original embedding features. We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system. Such mixing information is beneficial for extending the expressiveness of the latent features. Comprehensive experimentation on a real-world e-government dataset showed that our approach improved the performance significantly compared with the state-of-the-art baseline algorithms.

scholarly journals A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce

2018 ◽  
Vol 10 (12) ◽  
pp. 117 ◽  
Author(s):  
Bo Wang ◽  
Feiyue Ye ◽  
Jialu Xu
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Word Embedding ◽  
Purchase Behavior ◽  
Personalized Recommendation ◽  
Implicit Feedback ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Feedback Information ◽  
Recommendation Accuracy

A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user’s implicit feedback (BUIF). BUIF considers not only the user’s purchase behavior but also the user’s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user’s behavior log; calculated the user’s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item’s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms.

scholarly journals A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Fernando López Hernández ◽  
Elena Verdú Pérez ◽  
J. Javier Rainer Granados ◽  
Rubén González Crespo
Keyword(s):  
Text Mining ◽  
Collaborative Filtering ◽  
Implicit Feedback ◽  
Hybrid Filter ◽  
Content Filtering ◽  
Collaborative Filter ◽  
Content Selection

This paper addresses the problem of automatically customizing the sending of notifications in a nondisturbing way, that is, by using only implicit-feedback. Then, we build a hybrid filter that combines text mining content filtering and collaborative filtering to predict the notifications that are most interesting for each user. The content-based filter clusters notifications to find content with topics for which the user has shown interest. The collaborative filter increases diversity by discovering new topics of interest for the user, because these are of interest to other users with similar concerns. The paper reports the result of measuring the performance of this recommender and includes a validation of the topics-based approach used for content selection. Finally, we demonstrate how the recommender uses implicit-feedback to personalize the content to be delivered to each user.

scholarly journals Collaborative Filtering with Graph-based Implicit Feedback

2018 ◽  
Author(s):  
Minzhe Niu ◽  
Ruiming Tang ◽  
Yanru Qu ◽  
Xiuqiang He ◽  
Xuezhi Cao ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Implicit Feedback
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