An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Karpus, Aleksandra; Vagliano, Iacopo; Goczyła, Krzysztof; Morisio, Maurizio

doi:10.15439/2016f428

Cited by 9 publications

(4 citation statements)

References 33 publications

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“…However, in the case of web-scale recommendation tasks like social media, the Internet of Things (IoT), or various e-commerce applications, it is a hot topic also for model-based techniques, especially considering more complex and deep models [1], [19]. Another aspect that is particularly noticeable for collaborative filtering is related to the sparsity of user-item interactions [20]. Here, the quality of CF-based methods may be impacted by an insufficient number of items rated by each user [21].…”

Section: Related Workmentioning

confidence: 99%

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

Kannout¹,

Grodzki²,

Grzegorowski³

2022

Annals of Computer Science and Information Systems

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Although several approaches have been proposed throughout the last decade to build recommender systems (RS), most of them suffer from the cold-start problem. This problem occurs when a new item hits the system or a new user signs up. It is generally recognized that the ability to handle cold users and items is one of the key success factors of any new recommender algorithm. This paper introduces a frequent pattern mining framework for recommender systems (FPRS) -a novel approach to address this challenging task. FPRS is a hybrid RS that incorporates collaborative and content-based recommendation algorithms and employs a frequent pattern (FP) growth algorithm. The article proposes several strategies to combine the generated frequent itemsets with content-based methods to mitigate the cold-start problem for both new users and new items. The performed empirical evaluation confirmed its usefulness. Furthermore, the developed solution can be easily combined with any other approach to build a recommender system and can be further extended to make up a complete and standalone RS.Index Terms-recommendation system, cold-start problem, frequent pattern mining, quality of recommendations.

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Section: Related Workmentioning

confidence: 99%

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

Kannout¹,

Grodzki²,

Grzegorowski³

2022

Annals of Computer Science and Information Systems

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“…Another work that combined context-awareness, collaborative filtering and sequential pattern mining for generating learning resource recommendations was introduced [33]. In [34], different context dimensions are combined, such as location and time to implement a pre-filtering context approach based on two ontologies: recommender system context ontology and contextual ontological user profile ontology. In other words, the researchers applied prefiltering approach as a first step to generalize the user context and to project the context to a higher granularity level.…”

Section: A Contextual Pre-filteringmentioning

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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“…Because exact contexts normally do not have enough rating data (e.g., because of the known data sparsity problem in recommender systems), some works went beyond selecting training subsets form data that corresponds to the exact contextual values. For example, some works are based on ontologies such as the work by [15] which applies a prefiltering step that first identifies and generalizes a user context (i.e., projecting it to a higher granularity level). Afterwards, only data that corresponds to that context instance are selected for training.…”

Section: Incorporating Context Information Into Recommender Systemsmentioning

confidence: 99%

A Pre-Filtering Approach for Incorporating Contextual Information Into Deep Learning Based Recommender Systems

et al. 2020

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Depending on the Internet as the main source of information regarding all aspects of our life is becoming a trend. People seek relevant information, suggestions, and recommendations in an overloaded online world and through social ties regarding their daily activities, including places to visit and restaurants to try new food. The wide variety of choices that are available online causes information overloading, which thereby complicates the selection process. Traditional recommender systems are mostly dependent on a conventional model that is based on user-item-rating interaction without considering contextual information. We claim that new generations of recommendation systems able to exploit context in an innovative and efficient way is important and may statistically yield more significant rating predictions. However, only few research works have focused on how to effectively and efficiently exploit context metadata in Deep Learning (DL)-based recommendations. The main reason lies, perhaps most significantly, in the fact that most current DL algorithms are not intrinsically designed to incorporate contextual tags. In this paper, we provide a significant contribution for filling this gap by designing a hybrid algorithm that retrofits and repurposes a prefiltering contextual incorporation method and feeds the new dimension to a DL-based neural collaborative filtering method, thus preserving and recovering the benefits of both without their limitations. The paper also reports quantitative results that show that our method outperforms the baselines by statistically significant margins. INDEX TERMS Deep learning, recommender systems, collaborative filtering, context awareness, apache spark.

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An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Cited by 9 publications

References 33 publications

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

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

A Pre-Filtering Approach for Incorporating Contextual Information Into Deep Learning Based Recommender Systems

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