A Novel Approach to Enhance Personalized Recommendation

Recommendation techniques are important in the fields of E-commerce and other facilities like online shopping. One of the main problems is dynamically providing high-quality recommendation on less data. In this paper, a new dynamic personalized recommendation approach is introduced, in which information contained in both ratings and profile contents are utilized by inventing internal relations between ratings, a set of dynamic features are developed to describe user preferences in different phases , and then a recommendation is made by adaptively weighting the features. This approach performed well on public data sets.

[1]  Djemel Ziou,et al.  Long-term relevance feedback and feature selection for adaptive content based image suggestion , 2010, Pattern Recognit..

[2]  Byeong Man Kim,et al.  A new approach for combining content-based and collaborative filters , 2003, Journal of Intelligent Information Systems.

[3]  Jie Zhou,et al.  Stock Price Forecasting by Combining News Mining and Time Series Analysis , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[4]  Jim Z. C. Lai,et al.  A Fuzzy K-means Clustering Algorithm Using Cluster Center Displacement , 2009, J. Inf. Sci. Eng..

[5]  Lars Schmidt-Thieme,et al.  Factorizing personalized Markov chains for next-basket recommendation , 2010, WWW '10.

[6]  Marc Boullé,et al.  Comparing State-of-the-Art Collaborative Filtering Systems , 2007, MLDM.

[7]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[8]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[9]  Hans-Peter Kriegel,et al.  Ieee Transactions on Knowledge and Data Engineering Probabilistic Memory-based Collaborative Filtering , 2022 .

[10]  Alexey Tsymbal,et al.  The problem of concept drift: definitions and related work , 2004 .

[11]  Zhang Yu,et al.  Dynamic item-based recommendation algorithm with time decay , 2010, 2010 Sixth International Conference on Natural Computation.

[12]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[13]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[14]  Olga Papaemmanouil,et al.  Dynamic Recommendations in Internet Retailing , 2001, ECIS.

[15]  Jafar Ramadhan Mohammed.  Real-time implementation of an efficient RLS algorithm based on IIR filter for acoustic echo cancellation , 2008, 2008 IEEE/ACS International Conference on Computer Systems and Applications.

[16]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[17]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[18]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[19]  François Fouss,et al.  Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation , 2007, IEEE Transactions on Knowledge and Data Engineering.