Utilizing user tag-based interests in recommender systems for social resource sharing websites

Huang, Cheng-Lung; Yeh, Po-Han; Lin, Chengwei; Wu, Den-Cing

doi:10.1016/j.knosys.2013.11.001

Cited by 55 publications

(30 citation statements)

References 24 publications

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“…In order to evaluate our algorithm and to follow common practice in recommender systems research (e.g., [20,40]), we split our datasets into training and test sets. Therefore, we followed the method described in [27] to retain the chronological order of the posts.…”

Section: Evaluation Methods and Metricsmentioning

confidence: 99%

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

Seitlinger

Kowald

Kopeinik

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

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Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting nonlinear user-resource dynamics shaping attention and interpretation. In this paper, we propose a novel hybrid recommendation strategy that refines CF by capturing these dynamics. The evaluation results reveal that our approach substantially improves CF and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant.

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Section: Evaluation Methods and Metricsmentioning

confidence: 99%

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

Seitlinger

Kowald

Kopeinik

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

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show abstract

“…They also found that these approaches not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make the recommendation more easily interpretable. Huang et al [23] constructed a personalized user interest by incorporating frequency, recency and tag-based information and performed collaborative recommendations using user's social network in social resource sharing websites. Rawashdeh et al [24] showed a novel personalized search algorithm building two models of which one is user-tag relation model that reflects how a certain user assign tags which are similar to a given tag, the other one is tag-item relation model that captures how a certain tag is annotated to items which are similar to a given item.…”

Section: Categories Detailed Descriptionmentioning

confidence: 99%

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

et al. 2018

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Social tag information has been used by recommender systems to handle the problem of data sparsity. Recently, the relationships between users/items and tags are considered by most tag-induced recommendation methods. However, sparse tag information is challenging to most existing methods. In this paper, we propose an Extended-Tag-Induced Matrix Factorization technique for recommender systems, which exploits correlations among tags derived by co-occurrence of tags to improve the performance of recommender systems, even in the case of sparse tag information. The proposed method integrates coupled similarity between tags, which is calculated by the co-occurrences of tags in the same items, to extend each item's tags. Finally, item similarity based on extended tags is utilized as an item relationship regularization term to constrain the process of matrix factorization. MovieLens dataset and Book-Crossing dataset are adopted to evaluate the performance of the proposed algorithm. The results of experiments show that the proposed method can alleviate the impact of tag sparsity and improve the performance of recommender systems.

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“…The main target of recommendation systems is to make recommendations to users based on their interests and preferences [23,24]. Most of the recent research on recommendation systems focuses on combining different types of information.…”

Section: Related Workmentioning

confidence: 99%

Making recommendations by integrating information from multiple social networks

2016

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It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted

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Utilizing user tag-based interests in recommender systems for social resource sharing websites

Cited by 55 publications

References 24 publications

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

Making recommendations by integrating information from multiple social networks

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