Manuscript Number : CSEIT17253
Dual Access Cache Memory Management Recommendation Model Based on User Reviews
Authors(3) :-J. Sangeetha, Dr. V. Sinthu Janita Prakash, Dr. A. Bhuvaneswari
With the growing number of Internet services, the prediction of relevant services based on the user opinions is the major task in the recommendation model. The inclusion of important information taken from the user-generated textual reviews (in textual comments) is the major objective in the diverse review-based recommendation. An advanced textual analysis extracts the different elements for recommendation such as reviewer review, contextual information and the comparative opinions to improve the content-based recommendation performance. The major problems in such content-based recommendation are sparsity and memory management. Hence, this paper proposes the Dual Accessing Cache Memory Management (DACMM) to alleviate the issues in the recommendation system. The provision of ratings to the products based on the user experiences and their learning contribute to recommend the interesting product to the end users. With an increase in the dimensionality of products, the dimensionality of their reviews is increased. This paper employs the feature extraction that utilizes the positive, negative reviews with the overall word count to identify the features. The assigning of the index corresponds to the user review with the positive and negative features reduce the size of products into the interesting category. The integration of query processing with the cache memory management reduces the complexity in recommendation operation effectively. The comparative analysis between the proposed DACMM with the existing clustering and cache management models in terms of query latency, hit ratio, Mean Absolute Error (MAE), computational time and precision assure the effectiveness of the proposed DACMM in the review-based recommendation.
Associate Professor, Cauvery College for Women, Trichy, Tamil Nadu, India
Dr. V. Sinthu Janita Prakash
Head, Department of Computer Science, Cauvery College for Women, Trichy, Tamil Nadu, India
Dr. A. Bhuvaneswari
Associate Professor Cauvery College For Women, Trichy, Tamil Nadu, India
Cache Memory Management, Clustering, Feature Extraction, Indexing, Recommendation, Similarity.
- L. Chen, G. Chen, and F. Wang, "Recommender systems based on user reviews: the state of the art," User Modeling and User-Adapted Interaction, vol. 25, pp. 99-154, 2015.
- G. Ganu, Y. Kakodkar, and A. Marian, "Improving the quality of predictions using textual information in online user reviews," Information Systems, vol. 38, pp. 1-15, 2013.
- X. Chen, Z. Zheng, X. Liu, Z. Huang, and H. Sun, "Personalized qos-aware web service recommendation and visualization," IEEE Transactions on Services Computing, vol. 6, pp. 35-47, 2013.
- W. Yasin, H. Ibrahim, N. Hamid, and N. Udzir, "Windows web proxy caching simulation: A tool for simulating web proxy caching under windows operating systems," Journal of Computer Science, vol. 10, pp. 1380-1388, 2014.
- Y. S. Cho, S. C. Moon, S.-p. Jeong, I.-B. Oh, and K. H. Ryu, "Clustering method using item preference based on RFM for recommendation system in u-commerce," in Ubiquitous Information Technologies and Applications, ed: Springer, 2013, pp. 353-362.
- S. G. Esparza, M. P. O’Mahony, and B. Smyth, "Mining the real-time web: a novel approach to product recommendation," Knowledge-Based Systems, vol. 29, pp. 3-11, 2012.
- J. Cao, Z. Wu, Y. Wang, and Y. Zhuang, "Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation," Knowledge and information systems, vol. 36, pp. 607-627, 2013.
- W. Zhang, G. Ding, L. Chen, C. Li, and C. Zhang, "Generating virtual ratings from chinese reviews to augment online recommendations," ACM Transactions on intelligent systems and technology (TIST), vol. 4, p. 9, 2013.
- M. Daoud, S. Naqvi, and A. Ahmad, "Opinion Observer: Recommendation System on ECommerce Website," International Journal of Computer Applications, vol. 105, 2014.
- W. X. Zhao, J. Wang, Y. He, J.-R. Wen, E. Y. Chang, and X. Li, "Mining Product Adopter Information from Online Reviews for Improving Product Recommendation," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 10, p. 29, 2016.
- S. L. Addepalli, S. G. Addepalli, M. Kherajani, H. Jeshnani, and S. Khedkar, "A Proposed Framework for Measuring Customer Satisfaction and Product Recommendation for Ecommerce," International Journal of Computer Applications vol. 138, 2016.
- H. Liu, J. He, T. Wang, W. Song, and X. Du, "Combining user preferences and user opinions for accurate recommendation," Electronic Commerce Research and Applications, vol. 12, pp. 14-23, 2013.
- S. S. Htay and K. T. Lynn, "Extracting product features and opinion words using pattern knowledge in customer reviews," The Scientific World Journal, vol. 2013, 2013.
- V. K. Singh, R. Piryani, A. Uddin, and P. Waila, "Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification," in 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013, pp. 712-717.
- L. Chen and F. Wang, "Preference-based clustering reviews for augmenting e-commerce recommendation," Knowledge-Based Systems, vol. 50, pp. 44-59, 2013.
- Z. Zhou, M. Sellami, W. Gaaloul, M. Barhamgi, and B. Defude, "Data providing services clustering and management for facilitating service discovery and replacement," IEEE Transactions on Automation Science and Engineering, vol. 10, pp. 1131-1146, 2013.
- P. Moradi, S. Ahmadian, and F. Akhlaghian, "An effective trust-based recommendation method using a novel graph clustering algorithm," Physica A: Statistical Mechanics and its Applications, vol. 436, pp. 462-481, 2015.
- C.-L. Liao and S.-J. Lee, "A clustering based approach to improving the efficiency of collaborative filtering recommendation," Electronic Commerce Research and Applications, vol. 18, pp. 1-9, 2016.
- N. Korfiatis and M. Poulos, "Using online consumer reviews as a source for demographic recommendations: A case study using online travel reviews," Expert Systems with Applications, vol. 40, pp. 5507-5515, 2013.
- Y. S. Cho, S. C. Moon, S.-p. Jeong, I.-B. Oh, and K. H. Ryu, "Efficient purchase pattern clustering based on SOM for recommender system in u-commerce," in Ubiquitous Information Technologies and Applications, ed: Springer, 2014, pp. 617-626.
- J. Wang, E. Lo, M. L. Yiu, J. Tong, G. Wang, and X. Liu, "Cache design of ssd-based search engine architectures: an experimental study," ACM Transactions on Information Systems (TOIS), vol. 32, p. 21, 2014.
- R. Hu, W. Dou, and J. Liu, "ClubCF: A Clustering-Based Collaborative Filtering Approach for Big Data Application," IEEE Transactions on Emerging Topics in Computing, vol. 2, pp. 302-313, 2014.
- V. Formoso, D. Fernández, F. Cacheda, and V. Carneiro, "Using rating matrix compression techniques to speed up collaborative recommendations," Information retrieval, vol. 16, pp. 680-696, 2013.
- S. Gupta and S. Moharir, "Request Patterns and Caching for VoD Services with Recommendation Systems," arXiv preprint arXiv:1609.02391, 2016.
- M. Chaurasia and C. Satsangi, "Enhancing Proxy Server cache Management using Log Analysis and Recommendations," International Journal of Computer Applications, vol. 113, 2015.
- D. V. S. J. P. J. Sangeetha, "An Efficient Inclusive Similarity Based Clustering (ISC) Algorithm for Big Data," World Congress on Computing and Communication Technologies, 2016. Available: https://www.cs.uic.edu/liub/FBS/sentiment-analysis.html
Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 169-179
Manuscript Number : CSEIT17253
Publisher : Technoscience Academy
URL : http://ijsrcseit.com/CSEIT17253