Web browsers are provided with complex information space where the volume of information available to them is huge. There comes the Recommender system which effectively recommends web pages that are related to the current webpage, to provide the user with further customized reading material. To enhance the performance of the recommender systems, we include an elegant proposed web based recommendation system; Truth Discovery based Content and Collaborative RECommender (TDCCREC) which is capable of addressing scalability. Existing approaches such as Learning automata deals with usage and navigational patterns of users. On the other hand, Weighted Association Rule is applied for recommending web pages by assigning weights to each page in all the transactions. Both of them have their own disadvantages. The websites recommended by the search engines have no guarantee for information correctness and often delivers conflicting information. To solve them, content based filtering and collaborative filtering techniques are introduced for recommending web pages to the active user along with the trustworthiness of the website and confidence of facts which outperforms the existing methods. Our results show how the proposed recommender system performs better in predicting the next request of web users.

K. Latha1, P. Ramya2, V. Sita3, R. Rajaram4
Anna University of Technology, Tamil Nadu, India1, Anna University of Technology, Tamil Nadu, India2, Anna University of Technology, Tamil Nadu, India3, Thiagarajar College of Engineering, Tamil Nadu, India4

Recommendation, Content, Collaborative Filtering, Learning Automata, Navigation
Published By :
Published In :
ICTACT Journal on Soft Computing
( Volume: 1 , Issue: 2 )
Date of Publication :
October 2010

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