Service Recommendation Using Rating Inference Approach

There is large amount of information available on World Wide Web. But all information is not relevant to a particular user. So this is the place where recommender system is useful. Recommender system guides the user. But traditional recommender systems has some limitations. So, to overcome this limitations we are using a hybrid algorithm which gives accurate result. Which combines the collaborative filtering with sentimental analysis. Therefore, implementing the algorithm distributed will reduce the required computing time. Apache Hadoop is an open-source distributed computing framework that can be composed of a large number of low-cost hardware to run the application on a cluster. It provides applications with a set of stable and reliable interface, Use of single recommended method is difficult to meet the demand of a large amount of data and accuracy requirements.

[1]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[2]  Loren Terveen,et al.  PHOAKS: a system for sharing recommendations , 1997, CACM.

[3]  Hans-Peter Kriegel,et al.  Instance Selection Techniques for Memory-based Collaborative Filtering , 2002, SDM.

[4]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[5]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[6]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[7]  LindenGreg,et al.  Amazon.com Recommendations , 2003 .

[8]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[9]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.

[10]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[11]  Volker Markl,et al.  Scalable similarity-based neighborhood methods with MapReduce , 2012, RecSys.

[12]  Bo Pang,et al.  Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.

[13]  Ralf Lämmel,et al.  Google's MapReduce programming model - Revisited , 2007, Sci. Comput. Program..

[14]  Qing He,et al.  Parallel K-Means Clustering Based on MapReduce , 2009, CloudCom.

[15]  John Riedl,et al.  Combining Collaborative Filtering with Personal Agents for Better Recommendations , 1999, AAAI/IAAI.