Digital content recommendation system using implicit feedback data

2017 ◽  
Author(s):  
Gang Wu ◽  
Viswanathan Swaminathan ◽  
Saayan Mitra ◽  
Ratnesh Kumar
Keyword(s):  
Recommendation System ◽  
Digital Content ◽  
Implicit Feedback ◽  
Content Recommendation ◽  
Feedback Data

scholarly journals Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Huazhen Liu ◽  
Wei Wang ◽  
Yihan Zhang ◽  
Renqian Gu ◽  
Yaqi Hao
Keyword(s):  
Neural Network ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Heterogeneous Data ◽  
User Preference ◽  
Personalized Recommendation ◽  
Implicit Feedback ◽  
Network Training ◽  
Feedback Data ◽  
Explicit Feedback

Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.

scholarly journals Sistema de recomendación de contenidos para libros electrónicos inteligentes [Contents recommender system for intelligent eBooks]

2012 ◽  
Author(s):  
Edward Rolando Núnez Valdez ◽  
Vicente García Díaz ◽  
Jordan Pascual Espada ◽  
Carlos Enrique Montenegro Marín ◽  
Juan Manuel Cueva Lovelle ◽  
...  
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Implicit Feedback ◽  
Electronic Books ◽  
Collective Knowledge ◽  
Content Recommendation ◽  
Explicit Feedback

Resumen Un sistema de recomendación de contenidos para libros electrónicos inteligentes permite construir conocimientos colectivos para un conjunto de usuarios de una red social. Basándose en el análisis del comportamiento, preferencias y antecedentes de lectura, ayuda a los usuarios a descubrir contenidos interesantes relacionados a su perfil. En este trabajo, se propone un modelo para una plataforma de recomendación de contenidos basado en la retroalimentación implícita que ayude a los usuarios a descubrir contenidos de su interés de forma automática y dinámica. Palabras ClaveACRIE. GIUG, libros electrónicos, retroalimentación implícita, retroalimentación explicita, Sistemas de recomendación.   Abstract A content recommendation system for intelligent electronic books can build collective knowledge to a set of social network users. Based on the analysis of the behavior, preferences and background reading, helps users discover interesting content related to their profile. In this paper, we propose a model for a content recommendation platform based on implicit feedback to help users to discover content on their interest, automatically and dynamically. Keywords ACRIE, eBooks, GIUG, implicit feedback, explicit feedback, recommendation systems. 

Perbandingan Interpretasi Analisis Data Page View dan Click Related Post Pada Website Edukasi Sebagai Implisit Feedback Dengan Hasil Interpretasi Penggunaan Teknologi EyeTracking

2021 ◽  
Vol 3 (2) ◽  
pp. 66-72
Author(s):  
Riad Taufik Lazwardi ◽  
Khoirul Umam
Keyword(s):  
Decision Process ◽  
Recommendation System ◽  
User Behavior ◽  
Implicit Feedback ◽  
Decision Making Process ◽  
Learning Material ◽  
Relative Preferences ◽  
Insight Into ◽  
Explicit Feedback ◽  
Data Page

The analysis used in this study uses the help of Google Analytics to understand how the user's behavior on the Calculus learning material educational website page. Are users interested in recommendation articles? The answer to this question provides insight into the user's decision process and suggests how far a click is the result of an informed decision. Based on these results, it is hoped that a strategy to generate feedback from clicks should emerge. To evaluate the extent to which feedback shows relevance, versus implicit feedback to explicit feedback collected manually. The study presented in this study differs in at least two ways from previous work assessing the reliability of implicit feedback. First, this study aims to provide detailed insight into the user decision-making process through the use of a recommendation system with an implicit feedback feature. Second, evaluate the relative preferences that come from user behavior (user behavior). This differs from previous studies which primarily assessed absolute feedback. 

Personalized Content Recommendation Engine for Web Publishing Services Using Textmining and Predictive Analytics

2017 ◽  
pp. 113-124
Author(s):  
Başar Öztayşi ◽  
Ahmet Tezcan Tekin ◽  
Cansu Özdikicioğlu ◽  
Kerim Caner Tümkaya
Keyword(s):  
Recommendation System ◽  
Predictive Analytics ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Web Contents ◽  
Interest Level ◽  
History Of ◽  
Content Recommendation ◽  
Publishing Services ◽  
Web Publishing

Recommendation systems have become very important especially for internet based business such as e-commerce and web publishing. While content based filtering and collaborative filtering are most commonly used groups in recommendation systems there are still researches for new approaches. In this study, a personalized recommendation system based on text mining and predictive analytics is proposed for a real world web publishing company. The approach given in this chapter first preprocesses existing web contents, integrate the structured data with history of a specific user and create an extended TDM for the user. Then this data is used for prediction of the users interest in new content. In order to reach that point, SVM, K-NN and Naïve Bayesian methods are used. Finally, the best performing method is used for determining the interest level of the user in a new content. Based on the forecasted interest levels the system recommends among the alternatives.

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 Recommendation system for web article based on association rules and topic modelling

2017 ◽  
Vol 1 (1) ◽  
pp. 26-33 ◽  
Author(s):  
Guntur Budi Herwanto ◽  
Annisa Maulida Ningtyas
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
Primary Source ◽  
Information Discovery ◽  
Rule Mining ◽  
Topic Interest ◽  
Content Recommendation ◽  
The Right

The World Wide Web is now the primary source for information discovery. A user visits websites that provide information and browse on the particular information in accordance   with their   topic interest.   Through  the  navigational process,  visitors  often  had  to  jump  over  the  menu  to  find  the right  content.  Recommendation system can help the visitors to find the right content immediately.  In this study, we propose a two-level recommendation system, based on association rule and topic similarity.  We generate association rule by applying Apriori algorithm.   The  dataset  for  association  rule  mining  is a  session of  topics  that  made  by  combining  the  result of  sessionization and  topic  modeling.  On  the  other   hand,   the  topic  similarity made  by  comparing   the  topic  proportion of  web  article.  This topic proportion inferred from the Latent Dirichlet Allocation (LDA). The results show that in our dataset there are not many interesting   topic relations in one session.  This  result  can  be resolved,  by  utilizing  the  second  level  of  recommendation  by looking into the article  that  has the similar  topic.

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.

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