Behavioral similarities for collaborative recommendations

Esslimani, Ilham; Brun, Armelle; Bayer, Anne
December 2008
Journal of Digital Information Management;Dec2008, Vol. 6 Issue 6, p442
Academic Journal
Recommender systems contribute to the personalization of resources on web sites and information retrieval systems. These systems use different mining techniques to generate predictions corresponding to the needs of users. In this paper, we present a hybrid recommender system using a user-based approach, that combines predictions based on web usage patterns and rating data. We suggest a new technique that takes into account common usage patterns in order to compute correlations between users and select neighborhoods, without using any rating data. Unlike classical predictive systems that use directly mining techniques in order to suggest recommendations (like the Longest Common Subsequences technique or the frequent sequential patterns mining), the originality of our usage based technique is not to harness the discovered patterns to recommend resources, but to assess similarities of navigational users profiles. Recommendations are performed in a following step. The performance of our system is tested without and by combining predictions of both navigational based technique and classical collaborative filtering, in terms of accuracy and robustness. The experimentation put forward the impact of the navigational based technique on the performance of the hybrid recommender system in terms of precision and robustness. The tests show that the more the navigational based technique is involved in the recommendation process, the more the best predictions are accurate.


Related Articles

  • Walking on a User Similarity Network towards Personalized Recommendations. Gan, Mingxin // PLoS ONE;Dec2014, Vol. 9 Issue 12, p1 

    Personalized recommender systems have been receiving more and more attention in addressing the serious problem of information overload accompanying the rapid evolution of the world-wide-web. Although traditional collaborative filtering approaches based on similarities between users have achieved...

  • Mining Implicit Correlations between Users with the Same Role for Trust-Aware Recommendation. Haifeng Liu; Zhuo Yang; Jun Zhang; Xiaomei Bai; Wei Wang; Feng Xia // KSII Transactions on Internet & Information Systems;Dec2015, Vol. 9 Issue 12, p4892 

    Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally...

  • Mining Web Navigation Profiles For Recommendation System. AlMurtadha, Y. M.; Sulaiman, Md. N. B.; Mustapha, N.; Udzir, N. I. // Information Technology Journal;2010, Vol. 9 Issue 4, p790 

    No abstract available.

  • Mining Social Media for Knowledge Discovery. YEN, NEIL Y.; UYEN TRANG NGUYEN; JONG HYUK PARK // Computer Journal;Sep2015, Vol. 58 Issue 9, p1859 

    With ever-rising popularity of socialmedia, some sort of on-the-fly experiences are being learned and obtained by daily users. Despite a wide spectrum of research development efforts, how to facilitate knowledge discovery from an open environment and provide corresponding services remains an...

  • RECOMMENDER SYSTEMS IN SOCIAL NETWORKS. B. Jr, Cleomar Valois; de Oliveira, Marcius Armada // Revista de Gestão da Tecnologia e Sistemas de Informação / Jo;Sep-Dec2011, Vol. 8 Issue 3, p681 

    The continued and diversified growth of social networks has changed the way in which users interact with them. With these changes, what once was limited to social contact is now used for exchanging ideas and opinions, creating the need for new features. Users have so much information at their...

  • A FRAMEWORK FOR DEVELOPING CONTEXT-BASED BLOG CRAWLERS. Ferreira, Rafael; Lima, Rinaldo J.; Bittencourt, Ig Ibert; Filho, Dimas Melo; Holanda, Olavo; Costa, Evandro; Freitas, Fred; Melo, Lídia // Proceedings of the IADIS International Conference on WWW/Interne;Nov2010, p120 

    The development of the Web brought an interactive environment in which an increasing number of users are sharing their knowledge and opinions. Blogs are a growing part of this environment. Considering the rate at which knowledge is created daily in the blogosphere, such an amount of information...

  • A Hybrid Recommender System Guided by Semantic User Profiles for Search in the E-learning Domain. Zhuhadar, Leyla; Nasraoui, Olfa // Journal of Emerging Technologies in Web Intelligence;Nov2010, Vol. 2 Issue 4, p272 

    Various concepts, methods, and technical architectures of recommender systems have been integrated into E-commerce storefronts, such as Amazon.com, Netflix, etc. Thereby, recently, Web users have become more familiar with the notion of recommendations. Nevertheless, little work has been done to...

  • FARS: Fuzzy Ant based Recommender System for Web Users. Nadi, Shiva; Saraee, Mohammad H.; Jazi, Mohammad Davarpanah; Bagheri, Ayoub // International Journal of Computer Science Issues (IJCSI);Jan2011, Vol. 8 Issue 1, p203 

    Recommender systems are useful tools which provide an adaptive web environment for web users. Nowadays, having a user friendly website is a big challenge in e-commerce technology. In this paper, applying the benefits of both collaborative and content based filtering techniques is proposed by...

  • A Review and Classification of Recommender Systems Research. Deuk Hee Park; Il Young Choi; Hyea Kyeong Kim; Jae Kyeong Kim // International Proceedings of Economics Development & Research;2011, Vol. 5 Issue 1, p290 

    To understand the trend of recommender system researches by examining the published literature, and to provide practitioners and researchers with insight and future direction on recommender systems, we reviewed 164 articles on recommender systems from 31 journals which were published from 2001...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics