A Dynamic Personalized News Recommendation System Based on BAP User Profiling Method

Zhu, Zhiliang; Li, Deyang; Liang, Jie; Liu, Guoqi; Yu, Hai

doi:10.1109/access.2018.2858564

Cited by 19 publications

(14 citation statements)

References 27 publications

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“…The DRS systems excerpt their essential benefits by incorporating the methods for handling concept drift in data stream environments [43][44][45]. According to [46], there are two key approaches commonly used to handle concept drifts in any stream environments, namely, active and passive approaches.…”

Section: Types Of Concept Drifts Incorporated In Drssmentioning

confidence: 99%

“…The whole time is divided into static periods of time as short-terms which is challenging as user preference is dynamic as time increases continuously. 6 [36,45,75,102,133,134] Data stream Time-dept./ stream mining User prefer.…”

Section: Time-dependent Modelmentioning

confidence: 99%

“…In [135], a Long-/and Short-term algorithm (LOGO) was used to partition a users' reading historic data into multiple time slices to build both the long-/and short-term preference model using a time-sensitive weighting scheme. [45] provided the extension of this method to construct the user profile in a more efficient way, using different time functions and profiling methods. The experiment shows that the proposed method can provide a high quality of DRS results.…”

Section: Time-dependent Modelmentioning

confidence: 99%

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Recommender System Based on Temporal Models: A Systematic Review

et al. 2020

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Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Although the traditional techniques of RS have attained significant success in providing recommendations, they are insufficient in providing accurate recommendations due to concept drift problems. These issues have triggered a lot of researches on the development of dynamic recommender systems (DRSs) which is focused on the design of temporal models that will account for concept drifts and ensure more accurate recommendations. However, in spite of the several research efforts on the DRSs, only a few secondary studies were carried out in this field. Therefore, this study aims to provide a systematic literature review (SLR) of the DRSs models that can guide researchers and practitioners to better understand the issues and challenges in the field. To achieve the aim of this study, 87 papers were selected for the review out of 875 total papers retrieved between 2010 and 2019, after carefully applying the inclusion/exclusion and the quality assessment criteria. The results of the study show that concept drift is mostly applied in the multimedia domain, then followed by the e-commerce domain. Also, the results showed that time-dependent neighborhood models are the popularly used temporal models for DRS followed by the Time-dependent Matrix Factorization (TMF) and time-aware factors models, specifically Tensor models, respectively. In terms of evaluation strategy, offline metrics such as precision and recalls are the most commonly used approaches to evaluate the performance of DRS.

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Section: Types Of Concept Drifts Incorporated In Drssmentioning

confidence: 99%

Section: Time-dependent Modelmentioning

confidence: 99%

Section: Time-dependent Modelmentioning

confidence: 99%

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Recommender System Based on Temporal Models: A Systematic Review

et al. 2020

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“…Recommender systems have been applied in various areas, such as news [13][14][15], products [16], music [17], and fashion [18]. Collaborative filtering (CF) [2] focuses on preferences that may be similar between users; it is intuitive to recommend similar items for users with similar preferences.…”

Section: A Recommender Systems and Matrix Factorizationmentioning

confidence: 99%

“…We use filters in the convolution layer and each filter will map user preference feature according to different filter sizes. Let , denote the output features of the ith convolution of user preference feature for the jth filter, and denote the output feature maps of the jth filter of the convolution layer, as expressed in (13) and (14).…”

Section: Figure 2 the Architecture Of Proposed Merge-cnnmentioning

confidence: 99%

Rating Prediction Based on Merge-CNN and Concise Attention Review Mining

et al. 2020

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Online review websites provide an open platform for users to write reviews or give ratings on items (business services) as well as share their consumption experience. However, the volume of reviews is large, while the rating scores provide users with a quick picture of the items without reading all reviews. Recommendation systems can help users find items of interest by predicting user's ratings on unrated items. Review contents contain more personalized preference features than simply user ratings. Therefore, it is important to consider both ratings and review contents when making rating predictions. This research proposes a novel approach that combines deep learning and review mining with attention mechanism for rating predictions. Review mining with attention mechanism is adopted to extract concise attention reviews with important words and sentences. A merge convolutional neural network (merge-CNN) model is proposed to consider both the target user's preference features and performance features of target business for rating prediction. This method extracts quality business performance features from the quality reviews written by elite (credible) users. Moreover, the proposed method uses the concise attention reviews of target user's neighbors to simulate target users' reviews on unrated target business. Experiments were conducted on Yelp data sets to evaluate our proposed methods. The results show that the proposed method, i.e. considering concise attention reviews and quality reviews written by elite users, outperforms traditional methods in improving prediction accuracy. The experiment result also shows that our review simulation methods can well simulate target user's reviews on unrated target business.

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