Recent developments in affective recommender systems

Katarya, Rahul; Verma, Om Prakash

doi:10.1016/j.physa.2016.05.046

Cited by 49 publications

(23 citation statements)

References 81 publications

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“…Even though contextual information is critical rather than additional for location recommendation systems, it is not used in existing systems commonly and effectively. In the literature, it is emphasized that context-based algorithms are demanding for effective recommender systems [43][44][45][46].…”

Section: Related Workmentioning

confidence: 99%

“…Hybrid approaches combine at least two of the existing approaches and aim to minimize or remove the drawbacks of existing approaches, which may occur when they are used individually. erefore, hybrid systems, which are the combination of some of these approaches, can be the solution for a better recommendation system [43,44]. Even though some hybrid systems are presented in the literature, there are still untouched points for performance improvement of location recommender systems.…”

Section: Related Workmentioning

confidence: 99%

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Developing a Contextually Personalized Hybrid Recommender System

Bozanta

Kutlu

2018

Mobile Information Systems

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It is hard to choose places to go from an endless number of options for some specific circumstances. Recommender systems are supposed to help us deal with these issues and make decisions that are more appropriate. The aim of this study is to recommend new venues to users according to their preferences. For this purpose, a hybrid recommendation model is proposed to integrate user-based and item-based collaborative filtering, content-based filtering together with contextual information in order to get rid of the disadvantages of each approach. Besides that, in which specific circumstances the user will like a specific venue is predicted for each user-venue pair. Moreover, threshold values determining the user’s liking toward a venue are determined separately for each user. Results are evaluated with both offline experiments (precision, recall, F-1 score) and a user study. Both the experimental evaluation with a real-world dataset and a user study of the proposed system showed improvement upon the baseline approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Developing a Contextually Personalized Hybrid Recommender System

Bozanta

Kutlu

2018

Mobile Information Systems

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“…[49]. We will argue that the ability of such agents to align with the individual user cognitive phenomena determines a most promising development trend of such applications [14].…”

Section: Introductionmentioning

confidence: 98%

Cognitive Content Recommendation in Digital Knowledge Repositories – A Survey of Recent Trends

Skulimowski

2017

Artificial Intelligence and Soft Computing

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Abstract. This paper presents an overview of the cognitive aspects of content recommendation process in large heterogeneous knowledge repositories. It also covers applications to design algorithms of incremental learning of users' preferences, emotions, and satisfaction. This allows the recommendation procedures to align to the present and expected cognitive states of a user, increasing combined recommendation and repository use efficiency. The learning algorithm takes into account the results of the cognitive and neural modelling of users' decision behaviour. Inspirations from nature used in recommendation systems differ from the usual mimicking of biological neural processes. Specifically, a cognitive knowledge recommender may follow a strategy to discover emotional patterns in user behaviour and then adjust the recommendation procedure accordingly. The knowledge of cognitive decision mechanisms helps to optimize recommendation goals. Other cognitive recommendation procedures assist users in creating consistent learning or research groups. The anticipated primary application field of the above algorithms is a large knowledge repository coupled with an innovative training platform developed within the ongoing Horizon 2020 research project.

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“…In addition, it is more and more difficult to get the favourite crowdsourcing task for worker. Recommender system is an effective medium to solve the problem, which is used on many E-Commerce Platforms, such as Alibaba, Amazon, and Netflix [7]. But there are many problems which are not solved in recommender systems, such as similarity calculation, the lower recommended accuracy, data sparseness, and cold boot.…”

Section: Introductionmentioning

confidence: 99%

Recommendation of Crowdsourcing Tasks Based on Word2vec Semantic Tags

Dong

Wang

Cai

et al. 2019

Wireless Communications and Mobile Computing

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Crowdsourcing is the perfect show of collective intelligence, and the key of finishing perfectly the crowdsourcing task is to allocate the appropriate task to the appropriate worker. Now the most of crowdsourcing platforms select tasks through tasks search, but it is short of individual recommendation of tasks. Tag-semantic task recommendation model based on deep learning is proposed in the paper. In this paper, the similarity of word vectors is computed, and the semantic tags similar matrix database is established based on the Word2vec deep learning. The task recommending model is established based on semantic tags to achieve the individual recommendation of crowdsourcing tasks. Through computing the similarity of tags, the relevance between task and worker is obtained, which improves the robustness of task recommendation. Through conducting comparison experiments on Tianpeng web dataset, the effectiveness and applicability of the proposed model are verified.

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Recent developments in affective recommender systems

Cited by 49 publications

References 81 publications

Developing a Contextually Personalized Hybrid Recommender System

Developing a Contextually Personalized Hybrid Recommender System

Cognitive Content Recommendation in Digital Knowledge Repositories – A Survey of Recent Trends

Recommendation of Crowdsourcing Tasks Based on Word2vec Semantic Tags

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