Toward an effective hybrid collaborative filtering: A new approach based on matrix factorization and heuristic-based neighborhood

Alami, Yasser El Madani El; Nfaoui, El Habib; Beqqali, Omar El

doi:10.1109/isacv.2015.7105543

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Many recommendation system approaches have been proposed and developed in order to meet the growing needs of users and to overcome the encountered problems in the recommendation process. According to [2], three main types of recommendation systems have been proposed in the literature: collaborative filtering (CF) [11], recommendation systems based on the content [12], and hybrid recommendation systems [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Employing opposite ratings users in a new approach to collaborative filtering

Fazziki

Alami

Elhassouni

et al. 2022

IJEECS

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Over the past few decades, various recommendation system paradigms have been developed for both research and industrial purposes to satisfy the needs and preferences of users when they deal with enormous data. The collaborative filtering (CF) is one of the most popular recommendation techniques, although it is still immature and suffers from some difficulties such asparsity, gray sheep and scalability impeding recommendation quality. Therefore, we propose a new CF approach to deal with the gray sheep problem in order to improve the predictions accuracy. To realize this goal, our solution aims to infer new users from real ones existing in datasets. This transformation allows for creating users with opposite preferences to the real ones. On the one hand, our approach permits to amplify the number of neighbors, especially in the case of users who have unusual behavior (gray sheep). On the other hand, it facilitates building a dense similar neighborhood. The basic assumption behind this is that if user X is not similar to user Y, then the imaginary user ¬X is similar to the user Y. The performance of our approach was evaluated using two datasets, MovieLens and FilmTrust. Experimental results have shown that our approach surpasses many traditional recommendation approaches.

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Section: Introductionmentioning

confidence: 99%

Employing opposite ratings users in a new approach to collaborative filtering

Fazziki

Alami

Elhassouni

et al. 2022

IJEECS

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show abstract

“…CF ones can also recommend items with very different content, as long as other users have already shown interest for these different items. Among collaborative recommendation approaches, methods based on nearest neighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations [10][11][12]. CF models try to capture the interactions between users and items that produce the different rating values.…”

Section: Introductionmentioning

confidence: 99%

Normalizing Item‐Based Collaborative Filter Using Context‐Aware Scaled Baseline Predictor

Shi

Zhao

2017

Mathematical Problems in Engineering

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Item-based collaborative filter algorithms play an important role in modern commercial recommendation systems (RSs). To improve the recommendation performance, normalization is always used as a basic component for the predictor models. Among a lot of normalizing methods, subtracting the baseline predictor (BLP) is the most popular one. However, the BLP uses a statistical constant without considering the context. We found that slightly scaling the different components of the BLP separately could dramatically improve the performance. This paper proposed some normalization methods based on the scaled baseline predictors according to different context information. The experimental results show that using context-aware scaled baseline predictor for normalization indeed gets better recommendation performance, including RMSE, MAE, precision, recall, and nDCG.

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“…It was used in [70] to combine customer and product characteristics with behavioral data to build a hybrid recommender system for e-commerce. Blending is even possible with purely collaborative filters, for example by combining matrix factorization with neighborhood models [110,58] or bandit algorithms [101,218]. Fusion of user feedback types -explicit and implicit -has also proved effective [111,116].…”

Section: Hybrid Filteringmentioning

confidence: 99%

Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce

Paraschakis¹

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In memory of a beautiful soul ABSTRACTRecommender systems have become an integral part of virtually every e-commerce application on the web. The deployment of these expert systems has enabled users to quickly discover the products or services they need, at the same time increasing business revenues through better customer conversion. Remaining a very active research field since the mid-2000s, recommender systems have been modeled using a plethora of machine learning techniques. However, the adoptability of these models by industrial e-commerce platforms remains unclear.In this thesis, we assess the receptiveness of industrial platforms to algorithmic contributions of the research community by surveying more than 30 popular shopping cart solutions, and experimenting with various recommendation algorithms on proprietary e-commerce datasets.Another overlooked but important factor that complicates the design and use of recommender systems is their ethical implications. We provide a holistic view of these issues and summarize them in our ethical recommendation framework. This framework suggests new paradigm of ethics-awareness by design, and enables users to control sensitive moral aspects of recommendations via the proposed "ethical toolbox". The feasibility of this tool is supported by the results of our user study.Since the large part of moral implications stems from user profiling, we investigate algorithms capable of generating useragnostic recommendations based solely on a visited product page. We propose an ensemble learning scheme based on Thompson Sampling bandit policy, which models arms as base recommendation functions. We show how to adapt this algorithm to realistic situations when neither arm availability nor reward stationarity is guaranteed.

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Toward an effective hybrid collaborative filtering: A new approach based on matrix factorization and heuristic-based neighborhood

Cited by 3 publications

References 26 publications

Employing opposite ratings users in a new approach to collaborative filtering

Employing opposite ratings users in a new approach to collaborative filtering

Normalizing Item‐Based Collaborative Filter Using Context‐Aware Scaled Baseline Predictor

Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce

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