%0 Journal Article %1 V_2015 %A V, Ms.Sneha %A Raj, Mr. S Deepak %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K Hadoop Map-Reduce big collaborative data efficiency filtering keyword preference recommender scalability service system %N 3 %P 1099--1101 %R 10.17762/ijritcc2321-8169.150344 %T Implementation of Collaborative Filtering Approach in Preference Aware Service Recommendation %U http://dx.doi.org/10.17762/ijritcc2321-8169.150344 %V 3 %X Service recommendations are shown as remarkable tools for providing recommendations to users in an appropriate way. In the last few years, the number of customers, online information and services has grown very rapidly, resulting in the big data analysis problem for service recommendation system. Consequently, there is scalability and inefficiency problems associated with the traditional service recommendation system which suffers in processing or analyzing large-scale data. Moreover, most of available service recommendation system gives the same rankings and ratings of services to different users without any considerations of many user’s preferences, and hence it fails to reach user’s personalized requirements. In this paper, we have proposed a Preference-Aware Service Recommendation method, to overcome the above challenges. It aims at recommending the most appropriate and preferred services to the users and provide a personalized service recommendation list in an effective way. Here, users' preferences are captured as keywords, and a user-based Collaborative filtering approach is adopted to create appropriate recommendations. A widely-adopted distributed computing platform, Hadoop is used for the implementation of this approach, which improves its efficiency and scalability in big data environment, using the MapReduce parallel processing method.

@article{V_2015, abstract = {Service recommendations are shown as remarkable tools for providing recommendations to users in an appropriate way. In the last few years, the number of customers, online information and services has grown very rapidly, resulting in the big data analysis problem for service recommendation system. Consequently, there is scalability and inefficiency problems associated with the traditional service recommendation system which suffers in processing or analyzing large-scale data. Moreover, most of available service recommendation system gives the same rankings and ratings of services to different users without any considerations of many user’s preferences, and hence it fails to reach user’s personalized requirements. In this paper, we have proposed a Preference-Aware Service Recommendation method, to overcome the above challenges. It aims at recommending the most appropriate and preferred services to the users and provide a personalized service recommendation list in an effective way. Here, users' preferences are captured as keywords, and a user-based Collaborative filtering approach is adopted to create appropriate recommendations. A widely-adopted distributed computing platform, Hadoop is used for the implementation of this approach, which improves its efficiency and scalability in big data environment, using the MapReduce parallel processing method.}, added-at = {2015-08-06T09:00:48.000+0200}, author = {V, Ms.Sneha and Raj, Mr. S Deepak}, biburl = {https://www.bibsonomy.org/bibtex/21e22403694c35b2f911c28eb8f32bba9/ijritcc}, doi = {10.17762/ijritcc2321-8169.150344}, interhash = {675c02338bab6189025184908c7298b4}, intrahash = {1e22403694c35b2f911c28eb8f32bba9}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {Hadoop Map-Reduce big collaborative data efficiency filtering keyword preference recommender scalability service system}, month = {march}, number = 3, pages = {1099--1101}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-06T09:00:48.000+0200}, title = {Implementation of Collaborative Filtering Approach in Preference Aware Service Recommendation}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.150344}, volume = 3, year = 2015 }