An Intelligent Recommendation System for Individual and Group of Mobile Marketplace Users Based on the Influence of Items’ Features among User Profile

With the expansion of the wireless services, the number of mobile users has rapidly increased and the quantity of information on the internet and in wireless medium has also increased exponentially. This much information becomes difficult to be monitored by users well as searching for specific data may cost a lot in terms of time. Moreover, in the mobile scenario, only simple search and browsing functions are available due to limited computing power of a mobile phone. Therefore, it is more preferable to let the desirable information find the customers rather than to let them search for specific information when it is needed. This can be realized by integrating personalized user preferences along with justification recommendation system such that necessary information find the mobile user and alleviate the burden of search for that information when it is needed. This paper proposes an effective recommender system that can help consumers formulate better purchasing decision by incorporating support for user features. This system is not only recommends items to user based on their interest, but also attempt to recommend the nearest location place of the item. The proposed framework has the potentiality to increase consumer satisfaction, enhance consumer/company loyalty, and boost overall sales by giving justification and creditability to the products that have high degree of interest to consumers. The results show a significant promise in the system’s ability to make accurate recommendations to the users.

[1]  Raymond J. Mooney,et al.  Explaining Recommendations: Satisfaction vs. Promotion , 2005 .

[2]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[3]  Nick Antonopoulos,et al.  CinemaScreen recommender agent: combining collaborative and content-based filtering , 2006, IEEE Intelligent Systems.

[4]  Sergio Greco,et al.  Collaborative Filtering Supporting Web Site Navigation , 2004, AI Commun..

[5]  Songjie Gong.  A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering , 2010, J. Softw..

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

[7]  Akhmed Umyarov,et al.  Improving Collaborative Filtering Recommendations Using External Data , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[8]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[9]  Anna Lisa Gentile,et al.  A Hybrid Content-Collaborative Recommender System Integrated into an Electronic Performance Support System , 2007, 7th International Conference on Hybrid Intelligent Systems (HIS 2007).

[10]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[11]  Kam-Fai Wong,et al.  Interpreting TF-IDF term weights as making relevance decisions , 2008, TOIS.

[12]  Sung-Shun Weng,et al.  Feature-based recommendations for one-to-one marketing , 2003, Expert Systems with Applications.

[13]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[14]  Boris Mirkin,et al.  Mathematical Classification and Clustering , 1996 .

[15]  Lothar Thiele,et al.  A systematic comparison and evaluation of biclustering methods for gene expression data , 2006, Bioinform..

[16]  Liliana Ardissono,et al.  Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices , 2003, Appl. Artif. Intell..