A survey on context-aware recommender systems based on computational intelligence techniques

Abbas, Abdulla; Zhang, Limin; Khan, Samee U.

doi:10.1007/s00607-015-0448-7

Cited by 93 publications

(45 citation statements)

References 59 publications

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“…The authors define it as information, which is known a priori and characterised by additional related to the domain factors having a known hierarchical structure that does not change significantly over time. Due to great attention on this issue and many articles, that have appeared recently [1,6,7,9,11], finally, the Context-Aware Recommender System (CARS) field has been formed.…”

Section: Introductionmentioning

confidence: 99%

Contextual Modelling Collaborative Recommender System—Real Environment Deployment Results

Kużelewska

2016

Intelligent Decision Technologies 2016

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Nowadays, recommender systems are widely used in many areas as a solution to deal with information overload. There are some popular and effective methods to build a good recommendation system: collaborative filtering, content-based, knowledge-based and hybrid. Another approach, which made a significant progress over the last several years, are context-aware recommenders. There are many additional information related to the context or application area of recommender systems, which can be useful to generate accurate propositions, e.g. user localisation, items categories or attributes, a day of a week or time of a day, weather. Another issue is recommenders evaluation. Usually, they are only assessed with respect to their prediction accuracy (RMSE, MAE). This is good solution, due to possibility of off-line calculation. However, in real environment recommendation lists are finally evaluated by users who take into consideration many various factors, like novelty or diversity of items. In this article a multi-module collaborative filtering recommender system with consideration of context information is presented. The context is included both in post-filtering module as well as in a similarity measure. Evaluation was made off-line with respect to prediction accuracy and on-line, on real shopping platform.

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Section: Introductionmentioning

confidence: 99%

Contextual Modelling Collaborative Recommender System—Real Environment Deployment Results

Kużelewska

2016

Intelligent Decision Technologies 2016

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“…However, after this filtering step, a large number of high‐quality services persist. To refine the services that result from the selection phase and to provide the top‐rated ones, recommender systems have emerged as a next step that considers new filtering criteria other than QoS, such as active user's neighborhood, users' ratings, and context information (eg, time, location, etc). Recommender systems usually predict the top‐rated items by using a filtering technique, such as content‐based filtering, collaborative filtering, or hybrid filtering …”

Section: Introductionmentioning

confidence: 99%

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Fayala¹,

Mezni

2019

Concurrency and Computation

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Summary With the growing number of functionally similar services over the Internet, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, while in the real world, the perception and popularity of Web services may continually change. Time is becoming an increasingly important factor in recommender systems since time effects influence users' preferences to a large extent. In order to help users with this problem, we propose a time‐aware Web service recommendation system. First, we use K‐means clustering method in order to exclude the less similar users, which share few common Web services with the active user at different times. Slope One algorithm is also adopted in order to deal with data sparsity problem by predicting the missing ratings over time. Then, a recommendation algorithm is presented in order to recommend the top‐rated Web services. Experiments proved the accuracy of our approach compared to five existing solutions.

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“…Consequently, it is necessary to mention that even there have been developed several survey papers focused on recommender systems both regarding a wide point of view (Adomavicius and Tuzhilin [3], Konstan and Riedl [65], Bobadilla et al [21]), and also focused on specific areas (Campos et al [23], Klašnja-Milićević et al [63], Abbas et al [1], Martínez et al [83]), according to our best knowledge (October 2016), the current paper is the first effort focused on concentrating all the research works focused on recommender systems supported by fuzzy tools.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

143

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Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

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A survey on context-aware recommender systems based on computational intelligence techniques

Cited by 93 publications

References 59 publications

Contextual Modelling Collaborative Recommender System—Real Environment Deployment Results

Contextual Modelling Collaborative Recommender System—Real Environment Deployment Results

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Fuzzy Tools in Recommender Systems: A Survey

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