A deep neural network of multi-form alliances for personalized recommendations

Wang, Xuna; Tan, Qingmei; Zhang, Lifan

doi:10.1016/j.ins.2020.03.062

Cited by 13 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where |GT| represents all items in the test set, Number of Hits @k represents in the user's recommendation list, and the number of the top k items belonging to the test set. NDCG is often used to evaluate the accuracy of ranking of recommendation results [44]. NDCG introduces a location influence factor to discount lower ranked recommendations.…”

Section: Evaluation Metricsmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Sequential Recommendation Algorithm based on Short‐Sequence Enhancement and Temporal Self‐Attention Mechanism

Tang

Shen

et al. 2022

Complexity

View full text Add to dashboard Cite

Sequential recommendation algorithm can predict the next action of a user by modeling the user’s interaction sequence with an item. However, most sequential recommendation models only consider the absolute positions of items in the sequence, ignoring the time interval information between items, and cannot effectively mine user preference changes. In addition, existing models perform poorly on sparse data sets, which make a poor prediction effect for short sequences. To address the above problems, an improved sequential recommendation algorithm based on short-sequence enhancement and temporal self-attention mechanism is proposed in this paper. In the proposed algorithm, a backward prediction model is trained first, to predict the prior items in the user sequence. Then, the reverse prediction model is used to generate a batch of pseudo-historical items before the initial items of the short sequence, to achieve the goal of enhancing the short sequence. Finally, the absolute position information and time interval information of the user sequence are modeled, and a time-aware self-attention model is adopted to predict the user’s next action and generate a recommendation list. Various experiments are conducted on two public data sets. The experimental results show that the method proposed in this paper has excellent performance on both dense and sparse data sets, and its effect is better than that of the state of the art.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

“…However, the proposed model is trained offline and the computational time of the prediction is very fast. us, the computational complexity is not used as the evaluation metric in this study, which is a common way in the literature about the recommendation problem [19,44].…”

Section: Remarkmentioning

confidence: 99%

An Improved Sequential Recommendation Algorithm based on Short‐Sequence Enhancement and Temporal Self‐Attention Mechanism

Tang

Shen

et al. 2022

Complexity

View full text Add to dashboard Cite

show abstract

“…Therefore, videos that have been viewed were set as the sample set to automatically classify videos [21]. Naïve Bayes is a classification method featured by simple structure, accurate classification, fast operation, and stable performance [22,23]…”

Section: Automatic Recommendation Of College English Teaching Videosmentioning

confidence: 99%

Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands

Yang¹

2021

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

With the emergence of computers and networks, the social needs for English competence have presented a diversified and professionalized trend. The current single teaching model can no longer satisfy students’ needs. To cater to different demands of students and improve their level of satisfaction with personalized and automatically recommended teaching videos, an automatic recommendation sys-tem of college English teaching videos, which consists of interface layer, busi-ness logic layer, and data layer, was designed. The quality of recommended teaching videos was ensured through the strict management of English teaching videos within the system. The degree of interest in videos was calculated accord-ing to students’ browsing history of teaching videos. The content of teaching vid-eos that meet students’ personalized demands was established on the basis of the degree of interest. The Naïve Bayesian classification method was used to precise-ly, rapidly, and stably divide the teaching videos into two classes—interest and disinterest—according to the abovementioned information. Results show that the recall ratio and precision ratio of this system reach as high as 95.18% and 97.2%, respectively. The system recommendations averagely rank top, the recommenda-tion precision is high, and the recommended video contents are abundant, with an applause rate of 97.79%. This designed system can establish student-centered college English teaching methods, create a favorable language environment, and better promote the teaching of English among college students.

show abstract

“…Due to its strong learning ability on hidden features, it has been widely used in various recommendation systems [26,27,28,29].…”

Section: Related Workmentioning

confidence: 99%

A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

Zhang

Yan

et al. 2020

Preprint

View full text Add to dashboard Cite

In recent years, the number of online services has grown rapidly, invoke the required services through the cloud platform has become the primary trend.How to help users choose and recommend high-quality services among huge amounts of unused services has become a hot issue in research. Among the existing QoS prediction methods, the collaborative filtering(CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. Although existing deep learning methods could capture high-dimensional nonlinear features better, most of them only use the single feature of identity,

show abstract

A deep neural network of multi-form alliances for personalized recommendations

Cited by 13 publications

References 27 publications

An Improved Sequential Recommendation Algorithm based on Short‐Sequence Enhancement and Temporal Self‐Attention Mechanism

An Improved Sequential Recommendation Algorithm based on Short‐Sequence Enhancement and Temporal Self‐Attention Mechanism

Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands

A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

Contact Info

Product

Resources

About