Survey Paper on New Approach to Location Recommendation Using Scalable Content-Aware Collaborative Filtering

2019 ◽  
pp. 167-174
Author(s):  
Pooja Rajendra Pawale ◽  
Vidya Dhamdhere
Keyword(s):  
Collaborative Filtering ◽  
New Approach ◽  
Location Recommendation ◽  
Survey Paper ◽  
Content Aware

Scalable Content-Aware Collaborative Filtering for Location Recommendation

2018 ◽  
Vol 30 (6) ◽  
pp. 1122-1135 ◽  
Author(s):  
Defu Lian ◽  
Yong Ge ◽  
Fuzheng Zhang ◽  
Nicholas Jing Yuan ◽  
Xing Xie ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Location Recommendation ◽  
Content Aware

A new approach for rating prediction system using collaborative filtering

2018 ◽  
Vol 2 (2) ◽  
pp. 81-87 ◽  
Author(s):  
Pushpendra Kumar ◽  
Vinod Kumar ◽  
Ramjeevan Singh Thakur
Keyword(s):  
Collaborative Filtering ◽  
Prediction System ◽  
New Approach ◽  
Rating Prediction

New Fields in Classifying Algorithms for Content Awareness

2012 ◽  
Vol 7 (2) ◽  
pp. 1-15
Author(s):  
Radu-Dinel Miruta ◽  
Cosmin Stanuica ◽  
Eugen Borcoci
Keyword(s):  
Packet Classification ◽  
Tuple Space ◽  
New Approach ◽  
Data Packets ◽  
Server Side ◽  
The Media ◽  
Bit Vector ◽  
Edge Router ◽  
Content Aware

The content aware (CA) packet classification and processing at network level is a new approach leading to significant increase of delivery quality of the multimedia traffic in Internet. This paper presents a solution for a new multi-dimensional packet classifier of an edge router, based on content - related new fields embedded in the data packets. The technique is applicable to content aware networks. The classification algorithm is using three new packet fields named Virtual Content Aware Network (VCAN), Service Type (STYPE), and U (unicast/multicast) which are part of the Content Awareness Transport Information (CATI) header. A CATI header is inserted into the transmitted data packets at the Service/Content Provider server side, in accordance with the media service definition, and enables the content awareness features at a new overlay Content Aware Network layer. The functionality of the CATI header within the classification process is then analyzed. Two possibilities are considered: the adaptation of the Lucent Bit vector algorithm and, respectively, of the tuple space search, in order to respond to the suggested multi-fields classifier. The results are very promising and they prove that theoretical model of inserting new packet fields for content aware classification can be implemented and can work in a real time classifier.

scholarly journals Cognitive Similarity-Based Collaborative Filtering Recommendation System

2020 ◽  
Vol 10 (12) ◽  
pp. 4183 ◽  
Author(s):  
Luong Vuong Nguyen ◽  
Min-Sung Hong ◽  
Jason J. Jung ◽  
Bong-Soo Sohn
Keyword(s):  
Performance Evaluation ◽  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Cognitive Network ◽  
Data Set ◽  
New Approach ◽  
Layered Architecture ◽  
F Measure

This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.

scholarly journals COLLABORATIVE FILTERING: A NEW APPROACH TO SEARCHING DIGITAL LIBRARIES

2004 ◽  
Vol 10 (2) ◽  
pp. 177-191 ◽  
Author(s):  
Janet Webster ◽  
Seikyung Jung ◽  
Jon Herlocker
Keyword(s):  
Collaborative Filtering ◽  
Digital Libraries ◽  
New Approach
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