Content-based filtering for recommendation systems using multiattribute networks

Son, Jihoon; Kim, Seoung Bum

doi:10.1016/j.eswa.2017.08.008

Cited by 115 publications

(29 citation statements)

References 27 publications

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“…A novel CBF method is proposed that uses a multi-attribute network that considers different attributes when calculating correlations to recommend items to users [ 56 ]. Similarities are measured between directly and indirectly linked items.…”

Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence

2018

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A Recommender System (RS) is an intelligent system that assists users in finding the items of their interest (e.g. books, movies, music) by preventing them to go through huge piles of data available online. In an effort to overcome the data sparsity issue in recommender systems, this research incorporates a content based filtering technique with fuzzy inference system and a conformal prediction approach introducing a new framework called Hybrid Content based Fuzzy Conformal Recommender System (HCF-CRS). The proposed framework is implemented to be used in the domain of movies and it provides quality recommendations to users with a confidence level and an improved accuracy. In our proposed framework, first, a Content Based Filtering (CBF) technique is applied to create a user profile by considering the history of each user. CBF is useful in the situations like: lack of demographic information and the data sparsity problems. Second, a Fuzzy based technique is incorporated to find the similarities and differences between the user profile and the movies in the dataset using a set of fuzzy rules to get a predicted rating for each movie. Third, a Conformal prediction algorithm is implemented to calculate the non-conformity measure between the predicted ratings produced by fuzzy system and the actual ratings from the dataset. A p-value (confidence measure) is computed to give a level of confidence to each recommended item and a bound is set on the confidence level called a significance level ε, according to which the movies only above the specified significance level are recommended to user. By building a confidence centric hybrid conformal recommender system using the content based filtering approach with fuzzy logic and conformal prediction algorithm, the reliability and the accuracy of the system is considerably enhanced. The experiments are evaluated on MovieLens and Movie Tweetings datasets for recommending movies to the users and they are compared with other state-of-the-art recommender systems. Finally, the results confirm that the proposed algorithms perform better than the traditional ones.

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Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence

2018

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“…The approach compares user's preferences with new items' representations and matches user preferences with item attributes. There are some machine learning technologies that have been applied to the content-based filtering approach, such as naïve Bayes [23], [24].…”

Section: Content-based Filtering Approachmentioning

confidence: 99%

Movie genre preference prediction using machine learning for customer-based information

Wang

Zhang

2018

2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)

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Abstract-Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers' demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik-Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers' preferences with a small data set and design prediction tools for these enterprises.

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“…There are two types of recommender systems prominently used in the literature; contentbased recommendation (Besbes et al, 2015;Son and Kim, 2017) and collaborative filtering (Karabadji et al, 2018). Content-based recommendation stores the history of the product that a particular user has liked in the past and then, builds a user model to recommend a similar type of product that the user is most likely to prefer in the future.…”

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confidence: 99%

An integrated recommender system for improved accuracy and aggregate diversity

Bag

Ghadge

Tiwari

2019

Computers & Industrial Engineering

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Information explosion creates dilemma in finding preferred products from the digital marketplaces. Thus, it is challenging for online companies to develop an efficient recommender system for large portfolio of products. The aim of this research is to develop an integrated recommender system model for online companies, with the ability of providing personalized services to their customers. The K-nearest neighbors (KNN) algorithm uses similarity matrices for performing the recommendation system; however, multiple drawbacks associated with the conventional KNN algorithm have been identified. Thus, an algorithm considering weight metric is used to select only significant nearest neighbors (SNN). Using secondary dataset on MovieLens and combining four types of prediction models, the study develops an integrated recommender system model to identify SNN and predict accurate personalized recommendations at lower computation cost. A timestamp used in the integrated model improves the performance of the personalized recommender system. The research contributes to behavioral analytics and recommender system literature by providing an integrated decision-making model for improved accuracy and aggregate diversity. The proposed prediction model helps to improve the profitability of online companies by selling diverse and preferred portfolio of products to their customers.

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Content-based filtering for recommendation systems using multiattribute networks

Cited by 115 publications

References 27 publications

HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence

HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence

Movie genre preference prediction using machine learning for customer-based information

An integrated recommender system for improved accuracy and aggregate diversity

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