Developing intellectual patterns in online business to customer interaction with dynamic recommender system

Recommender systems are altering from novelties used by a small number of online sites, to grave business tools that are reshaping the world of E-commerce. Numerous sites are already using it to help their customers for finding good products to purchase. In this paper it presents an explanation of how recommender systems help E-commerce sites to increase sales, and analyze business patterns to developer. Based on the system business developer is helpful to build the product according to the customer likes. It create patterns using recommender systems, including the interfaces they present to customers, the technologies used to create the recommendations, and the inputs they need from customers. It concludes with ideas for new applications with recommender systems for business to customer interaction.

[1]  Christina Delimitrou,et al.  The Netflix Challenge: Datacenter Edition , 2013, IEEE Computer Architecture Letters.

[2]  Tengke Xiong,et al.  Combining Collaborative Filtering and Clustering for Implicit Recommender System , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[3]  Xu Chen,et al.  Adaptive Channel Recommendation for Opportunistic Spectrum Access , 2011, IEEE Transactions on Mobile Computing.

[4]  T. Hirave,et al.  Use of mining techniques to improve the effectiveness of marketing and sales , 2013, 2013 International Conference on Advances in Technology and Engineering (ICATE).

[5]  Yuan Zhang,et al.  Community-based user domain model collaborative recommendation algorithm , 2013 .

[6]  Yoon-Joo Park,et al.  The Adaptive Clustering Method for the Long Tail Problem of Recommender Systems , 2013, IEEE Transactions on Knowledge and Data Engineering.

[7]  Yang Guo,et al.  Bayesian-Inference-Based Recommendation in Online Social Networks , 2011, IEEE Transactions on Parallel and Distributed Systems.

[8]  Changsheng Xu,et al.  Cross-Space Affinity Learning with Its Application to Movie Recommendation , 2013, IEEE Transactions on Knowledge and Data Engineering.

[9]  Jurij F. Tasic,et al.  Affective Labeling in a Content-Based Recommender System for Images , 2013, IEEE Transactions on Multimedia.

[10]  Jia Li,et al.  A collaborative filtering recommendation algorithm based on user clustering and Slope One scheme , 2013, 2013 8th International Conference on Computer Science & Education.