WooKong: A Ubiquitous Accelerator for Recommendation Algorithms with Custom Instruction Sets on FPGA

2020 ◽  
pp. 1-1
Author(s):  
Chao Wang ◽  
Lei Gong ◽  
Xiang Ma ◽  
Xi Li ◽  
Xuehai Zhou
Keyword(s):  
Instruction Sets ◽  
Custom Instruction ◽  
Recommendation Algorithms

Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation

2021 ◽  
Vol 25 (4) ◽  
pp. 1013-1029
Author(s):  
Zeeshan Zeeshan ◽  
Qurat ul Ain ◽  
Uzair Aslam Bhatti ◽  
Waqar Hussain Memon ◽  
Sajid Ali ◽  
...  
Keyword(s):  
Recommendation System ◽  
Sources Of Information ◽  
Multiple Features ◽  
Recommendation Algorithms ◽  
Label Correlations ◽  
Feature Based ◽  
Multicriteria Model ◽  
Implicit And Explicit ◽  
Hybrid Recommendation ◽  
Different Sources

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.

scholarly journals A Two-Stage Neural Network-Based Cold Start Item Recommender

2021 ◽  
Vol 11 (9) ◽  
pp. 4243
Author(s):  
Chieh-Yuan Tsai ◽  
Yi-Fan Chiu ◽  
Yu-Jen Chen
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Service Providers ◽  
Cold Start ◽  
Computational Time ◽  
Two Stage ◽  
The Neural Network ◽  
Textual Description ◽  
Recommendation Algorithms ◽  
Content Information

Nowadays, recommendation systems have been successfully adopted in variant online services such as e-commerce, news, and social media. The recommenders provide users a convenient and efficient way to find their exciting items and increase service providers’ revenue. However, it is found that many recommenders suffered from the cold start (CS) problem where only a small number of ratings are available for some new items. To conquer the difficulties, this research proposes a two-stage neural network-based CS item recommendation system. The proposed system includes two major components, which are the denoising autoencoder (DAE)-based CS item rating (DACR) generator and the neural network-based collaborative filtering (NNCF) predictor. In the DACR generator, a textual description of an item is used as auxiliary content information to represent the item. Then, the DAE is applied to extract the content features from high-dimensional textual vectors. With the compact content features, a CS item’s rating can be efficiently derived based on the ratings of similar non-CS items. Second, the NNCF predictor is developed to predict the ratings in the sparse user–item matrix. In the predictor, both spare binary user and item vectors are projected to dense latent vectors in the embedding layer. Next, latent vectors are fed into multilayer perceptron (MLP) layers for user–item matrix learning. Finally, appropriate item suggestions can be accurately obtained. The extensive experiments show that the DAE can significantly reduce the computational time for item similarity evaluations while keeping the original features’ characteristics. Besides, the experiments show that the proposed NNCF predictor outperforms several popular recommendation algorithms. We also demonstrate that the proposed CS item recommender can achieve up to 8% MAE improvement compared to adding no CS item rating.

scholarly journals A social trust and preference segmentation-based matrix factorization recommendation algorithm

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Wei Peng ◽  
Baogui Xin
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
User Preference ◽  
Social Recommendation ◽  
Recommendation Algorithm ◽  
Commercial Activities ◽  
Trust Relationships ◽  
Recommendation Algorithms ◽  
The Difference

AbstractA recommendation can inspire potential demands of users and make e-commerce platforms more intelligent and is essential for e-commerce enterprises’ sustainable development. The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. To solve these problems mentioned above, we propose a social trust and preference segmentation-based matrix factorization (SPMF) recommendation algorithm. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly superior to that of some state-of-the-art recommendation algorithms. The SPMF algorithm is a better recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.

Reconfigurable Instruction-Set Application-Tuning for DSP

2003 ◽  
Vol 12 (03) ◽  
pp. 333-351 ◽  
Author(s):  
B. Mesman ◽  
Q. Zhao ◽  
N. Busa ◽  
K. Leijten-Nowak
Keyword(s):  
Current System ◽  
High Volume ◽  
Tool Support ◽  
Instruction Set ◽  
Instruction Sets ◽  
Application Characteristics ◽  
On Chip ◽  
Application Tuning ◽  
Primary Contribution ◽  
Hardware Efficiency

In current System-on-Chip (SoC) design, the main engineering trade-off concerns hardware efficiency and design effort. Hardware efficiency traditionally regards cost versus performance (in high-volume electronics), but recently energy consumption emerged as a dominant criterion, even in products without batteries. "The" most effective way to increase HW efficiency is to exploit application characteristics in the HW. The traditional way of looking at HW design tends to consider it a time-consuming and tedious task, however. Given the current lack of HW designers, and the pressure of time-to-market, clearly a desire exists to fine-balance the merits and effort of tuning your HW to your application. This paper discusses methods and tool support for HW application-tuning at different levels of granularity. Furthermore we treat several ways of applying reconfigurable HW to allow both silicon reuse and the ability to tune the HW to the application after fabrication. Our main focus is on a methodology for application-tuning the architecture of DSP datapaths. Our primary contribution is on reusing and generalizing this methodology to application-tuning DSP instruction sets, and providing tool support for efficient compilation for these instruction sets. Furthermore, we propose an architecure for a reconfigurable instruction-decoder, enabling application-tuning of the instruction-set after fabrication.

Instruction Sets

1999 ◽  
Author(s):  
Maya Gokhale ◽  
Judith D. Schlesinger
Keyword(s):  
Instruction Sets

Inventory Based Recommendation Algorithms

2020 ◽  
Author(s):  
Du Chen ◽  
Yuming Deng ◽  
Guangrui Ma ◽  
Hao Ge ◽  
Yunwei Qi ◽  
...  
Keyword(s):  
Recommendation Algorithms
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