A keyword-aware recommender system using implicit feedback on Hadoop

Hsieh, Meng-Yen; Weng, Tien‐Hsiung; Li, Kuan‐Ching

doi:10.1016/j.jpdc.2017.12.008

Cited by 20 publications

(8 citation statements)

References 20 publications

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“…In this section, the DIPMF framework is executed in MATLAB 2017b and its performance is evaluated with the KAR-IF [18], PARRA [20],…”

Section: Resultsmentioning

confidence: 99%

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Recommender System using Distributed Improved Predictive Framework with Matrix Factorization and Random Forest

2022

IJIES

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Online digital marketing achieves their revenue according to their advertisements or sales assignment when companies have the profitable attention for recommending their products to customers via ranking them. Online customers are not able to guarantee that the items delivered through the recommendation by big data are either comprehensive or applicable to their essentials. In the past few years, recommendation frameworks were broadly applied to analyze the massive amount of data. Among those, a Distributed Predictive model with Matrix factorization and random Forest (DPMF) has achieved high efficiency to predict the item ratings. However, it accounts only for user preferences and opinions whereas the other contextual data are necessary for enhancing the efficiency of rating prediction. In this article, a Distributed Improved Predictive model with a Matrix factorization and random Forest (DIPMF) framework is proposed that considers the elements of social context and the dynamic characteristic of every user for every item to enhance the quality of prediction. The primary aim is to combine the information from the preferences, opinions and social context of each user. The social context of users is multiple features of the context such as differences in the current opinion with earlier opinion, behavior, relationship and interaction. Since each user is connected through relations and interactions. At first, the training dataset is split into an optimal amount of splits for accelerating the parallel and distributed training process. Then, the training process is carried out by the DPMF with the Distributed Improved Predictive model with Matrix factorization-Improved variant (DIPMI) to create the representation of every user's preferences, opinions and social contexts in the training set. Further, the prediction of rating is formulated as a regression challenge and solved via the Random Forest (RF) algorithm that predicts the customer's rating behavior with their opinions and social context for every product. Finally, the experiments are conducted on trip advisor and Amazon datasets to evaluate the efficiency of DIPMI and DIPMF compared to the state-of-the-art recommendation frameworks. The findings exhibit that the DIPMF on the trip advisor dataset achieves an average of 0.6826 Root Mean Square Error (RMSE), 0.5925 Mean Absolute Error (MAE), 0.8369 recommendation quality and 0.0023 Confidence Range (CR) 95% compared to the other frameworks. Similarly, the DIPMF on Amazon dataset achieves an average of 0.7591 RMSE, 0.5704 MAE, 0.8298 recommendation quality and 0.0032 CR 95% compared to the other frameworks.

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“…In this section, the DIPMF framework is executed in MATLAB 2017b and its performance is evaluated with the KAR-IF [18], PARRA [20],…”

Section: Resultsmentioning

confidence: 99%

“…Here, the modified behavior principle of every customer and the random sampling was adopted. Keyword-Aware Recommendation using Implicit Feedback (KAR-IF) [18] was applied on products to solve the cold-start customers. The rating estimation and suggestion were established by the two server-side units with significant processes.…”

Section: Literature Surveymentioning

confidence: 99%

Recommender System using Distributed Improved Predictive Framework with Matrix Factorization and Random Forest

2022

IJIES

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“…Meng-Yen Hsieh et.al provided another method of solution for the cold start problem by exploring the web scraping [15] technique. Keyword Aware Recommender System will eliminate the cold start phase by introducing the technique of WEB crawling.…”

Section: Results In Cold-startmentioning

confidence: 99%

“…The major challenge of the recommender system is the Cold-start problem. As proposed by Meng-Yen Hsieh [15] Web crawling can be used to eliminate the cold start issue. But the challenge to be noted is, it may take extra time to become familiar with the core application and the scraping language needs to be adjusted.…”

Section: Identified Challengesmentioning

confidence: 99%

Recommendation System: A Systematic Overview on Methods, Issues and Solutions

2020

IJATCSE

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As the technology evolves, the Recommendation System (RS) is becoming a trending topic and widely applied in many fields such as e-commerce sites, health care, stock market, movie,song recommendation. This paper entirely focuses on various methodologies which is helpful to build a recommendation and also the challenges when developing a recommender system. In an e-business site when the user selects any product, then the recommendation will be provided by considering the relationships of user-user, user-item, item-item. In this paper, various methodologies adopted in the existing works are reviewed and summarized with its merits and detriments.Also, it will support the researchers and developers of the recommender system by providing a systematic review of the state-of-the-art recommender systems.This paper gives a survey about the recommendation system, phases of recommendation, filtering techniques, challenges, and some of the research paper which could solve those challenges.

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“…Also, Agarwal and Singhal [21] propose a solution based on a domain ontology, which uses explicit and implicit data of users; the registered user provides the explicit information, while the implicit information includes mouse behavior and user session data. e system proposed in [22] retrieves keywords from external user and 2 Scientific Programming item sources to generate implicit-feedback to diminish the cold-start problem. However, although using implicit-feedback and explicitfeedback can enhance recommendations, there are situations in which users are not willing to provide explicitfeedback.…”

Section: State-of-the-artmentioning

confidence: 99%

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

Hernández

Pérez

Granados

et al. 2019

Scientific Programming

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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.

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A keyword-aware recommender system using implicit feedback on Hadoop

Cited by 20 publications

References 20 publications

Recommender System using Distributed Improved Predictive Framework with Matrix Factorization and Random Forest

Recommender System using Distributed Improved Predictive Framework with Matrix Factorization and Random Forest

Recommendation System: A Systematic Overview on Methods, Issues and Solutions

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

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