Context-Aware Recommender Systems

Adomavicius, Gediminas; Tuzhilin, Alexander

doi:10.1007/978-0-387-85820-3_7

Cited by 923 publications

(610 citation statements)

References 46 publications

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“…However, also other parameters may be considered, such as company (alone, with friends, with girlfriend) which may be relevant when recommending movies or vacations. In addition, the exact context sometimes can be too narrow, as Adomavicius and Tuzhilin [2] exemplified by considering the context of watching a movie with a girlfriend in a movie theater on Saturday. Using this exact context may be problematic for several reasons.…”

Section: Contextual Modelingmentioning

confidence: 99%

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

Karpus

Vagliano

Goczyła

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-Context-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation. However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction. This is known as the data sparsity problem. Moreover, often user preference representation depends on the domain or the specific recommendation approach used. Therefore, a big effort is required to change the method used. In this paper we present a new approach for contextual pre-filtering (i.e. using the current context to select a relevant subset of data). Our approach can be used with existing recommendation algorithms. It is based on two ontologies: Recommender System Context ontology, which represents the context, and Contextual Ontological User Profile ontology, which represents user preferences. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can significantly improve the accuracy of prediction.

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Section: Contextual Modelingmentioning

confidence: 99%

“…We distinguish three forms of context-aware recommendation process: contextual pre-filtering, contextual post-filtering and contextual modeling [2]. Pre-filtering approaches use the current context to select a relevant subset of data on which recommendation algorithm is applied.…”

Section: Introductionmentioning

confidence: 99%

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

Karpus

Vagliano

Goczyła

et al. 2016

Annals of Computer Science and Information Systems

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“…In many cases, the research on CARS remains conceptual [3], where a certain method has been developed, but testing is limited to an offline evaluation or a shortterm user test with only a handful of people, often students or colleagues who are not representative for the population. In contrast, this research investigates the role of context for news recommendations by means of a large-scale empirical study.…”

Section: Related Workmentioning

confidence: 99%

“…Contextual information is used to determine the relevance of the feedback and filter these data based on the current situation. For instance, if a user wants to read news during the evening, an exact pre-filter [3] selects only feedback gathered during the evening to calculate the recommendations. Therefore, the day is partitioned into four non-overlapping intervals: morning from 6:00 to 11:00, daytime from 11:00 to 12:00 and from 13:00 to 18:00, noon from 12:00 to 13:00 and evening/night from 18:00 to 6:00.…”

Section: Context-aware Content-based Recommendationsmentioning

confidence: 99%

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A Personalized and Context-Aware News Offer for Mobile Devices

Pessemier

Vanhecke

Martens

2016

Lecture Notes in Business Information Processing

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For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer.

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Applying knowledge flow mining to group recommendation methods for task‐based groups

Lai

2014

Asso for Info Science & Tech

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In a knowledge-intensive environment, a task in an organization is typically performed by a group of people who have task-related knowledge and expertise. Each group may require task-related knowledge of different topic domains and documents to accomplish its tasks. Document recommendation methods are very useful to resolve the information overload problem and proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. A worker's document referencing behavior can be modeled as a knowledge flow (KF) to represent the evolution of his information needs over time. However, the information needs of workers and groups may change over time, so that modeling the knowledge referencing behavior of a group of workers is difficult. Additionally, most traditional recommendation methods which provide personalized recommendations do not consider workers' KFs, or the information needs of the majority of workers in a group to recommend task knowledge. In this work, I integrate the KF mining method and propose group-based recommendation methods, including group-based collaborative filtering (GCF) and group content-based filtering (GCBF), to actively provide task-related documents for groups. Experimental results show that the proposed methods have better performance than the personalized recommendation methods in recommending the needed documents for groups. Thus, the recommended documents can fulfill the groups' task needs and facilitate knowledge sharing among groups.

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Context-Aware Recommender Systems

Cited by 923 publications

References 46 publications

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

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

A Personalized and Context-Aware News Offer for Mobile Devices

Applying knowledge flow mining to group recommendation methods for task‐based groups

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