CRecSys: A Context-Based Recommender System Using Collaborative Filtering and LOD

Sejwal, Vineet Kumar; Abulaish, Muhammad; Jahiruddin,

doi:10.1109/access.2020.3020005

Cited by 17 publications

(5 citation statements)

References 43 publications

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“…Some researches was established in customizing an rating scheme in recommender systems such as Weighted Average Ratings which was used in IMDb rating [27], Bayesian Average rating [28], Median Average Rating [29], Mean Rating [30], Normalized Rating Frequency [31]. In other research, rating value was also de ned from contextual data such as aspect category [32], word embedding representation of reviews [23], and Linked Open Data [33]. Hence, rating prediction is a main task that a recommender system is asked to achieve [34].…”

Section: Related Workmentioning

confidence: 99%

New Custom Rating for Improving Recommendation System Performance

Fahrudin,

Wijaya

2024

Preprint

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Nowadays, the recommendation system still attracts many researchers. Various new businesses focusing on online marketing (E-Commerce) have emerged since the covid-19 pandemic. This phenomenon allows recommending items based on Collaborative Filtering Recommender System (CF) to enhance the shopping experience for users. An accurate CF depends on how similarity algorithms can find similar profile users correctly. The traditional similarity measures are based on the user-item rating matrix. In this paper, we employ four custom rating combine with new rating formula from the popularity of users, items, and its original rating which called called New Custom Rating (Ncr). Instead of focusing on original rating only, our Ncr tried to maximize the recommender system's performance by employing users' popularity and items to derive new ratings value. Using those formulas will make the new value rating more representative and the calculation of the similarity algorithm more precisely. Finally, the increased accuracy of the recommendation systems will be achieved. We conduct Ncr in four custom rating algorithms and four algorithms of the reccomender system in five public data sets. The experimental results demonstrate that Ncr can increase the recommendation system's accuracy by decreasing RMSE, MSE, and MAE and increasing FCP and Hit Rate. Ncr may improved accuracy of the reccomender system in various recommender systems algorithms by employing the popularity of users and items in rating.

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Section: Related Workmentioning

confidence: 99%

New Custom Rating for Improving Recommendation System Performance

Fahrudin,

Wijaya

2024

Preprint

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“…In the attribute representation step, ℱ is filtered by 𝐷 𝑡𝑟𝑎𝑖𝑛 which involves 𝑦 𝑗 ∈ ℝ and 𝑥 𝑗 ∈ ℝ 𝛽+1 . By using the filtered forest, for any observation 𝑥 𝑗 , 𝑗 = 1, … , 𝑁, the prediction from each tree in ℱ is obtained as: 𝑓 𝑗 = 𝑓(𝑥 𝑗 ; 𝜃) = (𝑇 𝑗 (𝑥 𝑗 ; 𝜃 1 ), … , 𝑇 𝑀 (𝑥 𝑗 ; 𝜃 𝑀 )) 𝑇 (4) In Eq. ( 4), 𝑇 𝑀 (𝑥 𝑗 ; 𝜃 𝑀 ) = 𝑦 ̂𝑗𝑚 is the binary prediction of observation 𝑥 𝑗 given by ℐ 𝑚 .…”

Section: Fdnn-based Learning and Predictionmentioning

confidence: 99%

“…Digital marketing, e-government, e-commerce, e-learning, and other real-time applications are exemplars. Collaborative Filtering (CF) is the most beneficial strategy for sentiment analysis [4]. This strategy is usually subcategorized into model-based and memory-based CFs.…”

Section: Introductionmentioning

confidence: 99%

Distributed Improved Deep Prediction for Recommender System using an Ensemble Learning

Muruganantham

Shanmugasundaram²

2023

IJRITCC

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If online businesses possess valuable interest for suggesting their items by scoring them, then digital advertising gains their profits depending on their promotions or marketing task. Web users cannot be certain that the products handled via big-data recommendation are either advanced or interesting to their needs. In recent decades, recommender system models have been widely used to analyses large quantities of information. Amongst, a Distributed Improved Prediction with Matrix Factorization (MF) and Random Forest (RF) called DIPMF model exploits individual’s desires, choices and social context together for predicting the ratings of a particular item. But, the RF scheme needs high computation power and time for learning process. Also, its outcome was influenced by the training parameters. Hence this article proposes a Distributed Improved Deep Prediction with MF and ensemble learning (DIDPMF) model is proposed to decrease the computational difficulty of RF learning and increasing the efficiency of rating prediction. In this DIDPMF, a forest attribute extractor is ensemble with the Deep Neural Network (fDNN) for extracting the sparse attribute correlations from an extremely large attribute space. So, incorporating RF over DNN has the ability to provide prediction outcomes from all its base trainers instead of a single estimated possibility rate. This fDNN encompasses forest module and DNN module. The forest module is employed as an attribute extractor to extract the sparse representations from the given raw input data with the supervision of learning outcomes. First, independent decision trees are constructed and then ensemble those trees to obtain the forest. After, this forest is fed to the DNN module which acts as a learner to predict the individual’s ratings with the aid of novel attribute representations. Finally, the experimental results reveal that the DIDPMF outperforms than the other conventional recommender systems.

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“…In context-aware filtering, the contextual information is used to filter the input data before (or after) generating recommendations [7]. Context-aware recommendation systems (CARS) have shown significant impact in many applications where the accuracy of forecasting tasks is essential for more precise personalized recommendations [8].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Nawara

Kashef

2021

IEEE Access

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An essential goal of recommendation systems is to provide users with accurate and personalized recommendations that meet their preferences. With the rapid growth of IoT-connected sensors, the availability of contextual information has increased, and this has necessitated the fast development of Context-Aware Recommendation Systems (CARS). Context-Aware recommenders are different from traditional recommenders because of their ability to predict the ratings of target users/items by exploiting the knowledge of contextual information. Context-aware recommenders define the context as any information that characterizes the situations of items and users at a particular interaction. They are essential for some contexts where prediction can be more precise in generating specific personalized recommendations. This paper provides a comprehensive review of context-based recommendation systems in IoT environments, namely IoT-CARS, and sheds light on their requirements, characteristics, and applications. We characterize context-aware recommenders in terms of the different IoT contexts and how these contexts are modeled. We also highlight the used metrics to evaluate the performance of various context-based recommenders.

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CRecSys: A Context-Based Recommender System Using Collaborative Filtering and LOD

Cited by 17 publications

References 43 publications

New Custom Rating for Improving Recommendation System Performance

New Custom Rating for Improving Recommendation System Performance

Distributed Improved Deep Prediction for Recommender System using an Ensemble Learning

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

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