Tag-aware recommender systems based on deep neural networks

Zuo, Yi; Zeng, Jiulin; Gong, Maoguo; Jiao, Licheng

doi:10.1016/j.neucom.2015.10.134

Cited by 111 publications

(64 citation statements)

References 43 publications

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“…But user-defined tags undergo various challenges such as ambiguity, redundancy and sparsity. To deal with these challenges, deep neural networks extract the information from tags and process the information through multiple layers to retrieve more advanced and abstract data [41]. Authors in [42] have provided a very short survey of deep learning methods used in RSs.…”

Section: Deep Learning In Recommender Systemsmentioning

confidence: 99%

“…Moreover, deep learning has also opened the doors for improving the accuracy of social recommender systems. [19], [37], [44], [46], [52] 2016 17 [16], [21], [24], [25], [28], [36], [40], [41], [45], [47], [57], [60], [61], [69], [70], [65], [71] 2017 20 [17], [18], [20], [22], [26], [27], [38], [42], [43], [51], [53], [62]- [64], [66], [68], [72]- [74], [78] VI. CONCLUSION A voluminous research has been done and is also proceeding in recommender systems using deep learning.…”

Section: Miscellaneousmentioning

confidence: 99%

See 1 more Smart Citation

An Application-oriented Review of Deep Learning in Recommender Systems

Shokeen¹,

Rana²

2019

IJISA

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The development in technology has gifted huge set of alternatives. In the modern era, it is difficult to select relevant items and information from the large amount of available data. Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years. But recommender systems implementing these algorithms suffer from various challenges. Deep learning is proved successful in speech recognition, image processing and object detection. In recent years, deep learning has been also proved effective in handling information overload and recommending items. This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications. The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.

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Section: Deep Learning In Recommender Systemsmentioning

confidence: 99%

Section: Miscellaneousmentioning

confidence: 99%

An Application-oriented Review of Deep Learning in Recommender Systems

Shokeen¹,

Rana²

2019

IJISA

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“…Pitsilis et al [43] presented an item recommendation algorithm which is based on tag clustering and the proposed notion Annotation Competency. Zuo et al [44] proposed an item recommendation algorithm in which users’ profiles are initially represented by tags and then a deep neural network model is used to extract the in-depth features from tag space. After users’ profiles are updated by the extracted features, user-based CF is used to predict the items that users may like.…”

Section: Related Workmentioning

confidence: 99%

A framework for diversifying recommendation lists by user interest expansion

Zhang

Zheng

Zeng

2016

Knowledge-Based Systems

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Recommender systems have been widely used to discover users’ preferences and recommend interesting items to users during this age of information load. Researchers in the field of recommender systems have realized that the quality of a top-N recommendation list involves not only relevance but also diversity. Most traditional recommendation algorithms are difficult to generate a diverse item list that can cover most of his/her interests for each user, since they mainly focus on predicting accurate items similar to the dominant interests of users. Additionally, they seldom exploit semantic information such as item tags and users’ interest labels to improve recommendation diversity. In this paper, we propose a novel recommendation framework which mainly adopts an expansion strategy of user interests based on social tagging information. The framework enhances the diversity of users’ preferences by expanding the sizes and categories of the original user-item interaction records, and then adopts traditional recommendation models to generate recommendation lists. Empirical evaluations on three real-world data sets show that our method can effectively improve the accuracy and diversity of item recommendation.

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“…This usually results in sparse, redundant, and ambiguous tag information, and significantly weakens the performance of content-based recommendation systems. The common solution is to apply machine learning techniques, e.g., clustering [14] or autoencoders [17], to learn more abstract and representative features from raw tags. Recently, Xu et al [16] propose a deep-semantic model called DSPR which utilizes deep neural networks to model abstract and recommendationoriented representations for social tags.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation

Lukasiewicz

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web. To alleviate the cold start problem, previous approaches have incorporated various additional sources of information into traditional matrix factorization models. These upgraded models, however, achieve only "marginal" enhancements on the performance of personalized recommendation. Therefore, inspired by the recent development of deep-semantic modeling, we propose a hybrid deep-semantic matrix factorization (HDMF) model to further improve the performance of tag-aware personalized recommendation by integrating the techniques of deep-semantic modeling, hybrid learning, and matrix factorization. Experimental results show that HDMF significantly outperforms the state-of-the-art baselines in tag-aware personalized recommendation, in terms of all evaluation metrics, e.g., its mean reciprocal rank (resp., mean average precision) is 1.52 (resp., 1.66) times as high as that of the best baseline.

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Tag-aware recommender systems based on deep neural networks

Cited by 111 publications

References 43 publications

An Application-oriented Review of Deep Learning in Recommender Systems

An Application-oriented Review of Deep Learning in Recommender Systems

A framework for diversifying recommendation lists by user interest expansion

Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation

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