Enhanced content-based filtering algorithm using Artificial Bee Colony optimisation

Recommender systems can guide the users in a tailored way to interesting objects in a large space of possible options. The Content-based Filtering (CBF) approach is one of the most widely adapted to date. It analyses a set of textual descriptions of items. These items are already evaluated by an interactive user in prior steps. It then builds a model or profile of this user. The profile is then exploited to suggest a new item. Unfortunately, filtering in this method is mainly used for recommending only one item at a time. The research here considers how this component can propose a list of items to a user from large amounts of data. We enhance the Content-based Filtering algorithm in order to explore a huge data set and return a list of recommendations rather than just rating an item. For this, an Artificial Bee Colony (ABC) technique has been adapted and applied to the CBF method. ABC is one of the efficient Evolutionary Computing techniques that are used in solving optimization problems.

[1]  Robin van Meteren.  Using Content-Based Filtering for Recommendation , 2000 .

[2]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[3]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[4]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[5]  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.

[6]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[7]  Phil Husbands,et al.  An Introduction to Evolutionary Computing for Musicians , 2007 .

[8]  Thorsten Joachims,et al.  A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization , 1997, ICML.

[9]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[10]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[11]  Chang Liu,et al.  A New Path Planning Method Based on Firefly Algorithm , 2012, 2012 Fifth International Joint Conference on Computational Sciences and Optimization.

[12]  K. Al-Sultan,et al.  A Genetic Algorithm for the Set Covering Problem , 1996 .

[13]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[14]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[15]  Xinghua Wu,et al.  A density adjustment based particle swarm optimization learning algorithm for neural network design , 2011, 2011 International Conference on Electrical and Control Engineering.

[16]  Zhifang Liao,et al.  Content-Based Filtering Recommendation Algorithm Using HMM , 2012, 2012 Fourth International Conference on Computational and Information Sciences.

[17]  You Yang,et al.  An improved particle swarm optimization algorithm , 2010, 2013 Ninth International Conference on Natural Computation (ICNC).

[18]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[19]  Christian Blum,et al.  Ant colony optimization: Introduction and recent trends , 2005 .

[20]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[21]  H. Soneji,et al.  Towards the improvement of Cuckoo search algorithm , 2012, 2012 World Congress on Information and Communication Technologies.

[22]  Annupan Rodtook,et al.  Migration planning using modified Cuckoo Search Algorithm , 2013, 2013 13th International Symposium on Communications and Information Technologies (ISCIT).

[23]  Nurhan Karaboga,et al.  A new design method based on artificial bee colony algorithm for digital IIR filters , 2009, J. Frankl. Inst..

[24]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation.(Special Section: Recommender Systems) , 1997 .

[25]  Pasquale Lops,et al.  Introducing Serendipity in a Content-Based Recommender System , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[26]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[27]  Jeng-Shyang Pan,et al.  Overview of Algorithms for Swarm Intelligence , 2011, ICCCI.

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

[29]  ScienceDirect,et al.  Advances in engineering software , 2008, Adv. Eng. Softw..

[30]  Sahin Albayrak.  A Hybrid Approach to Recommender Systems based on Matrix Factorization , 2009 .

[31]  Huiyou Chang,et al.  The Discrete Binary Version of the Improved Particle Swarm Optimization Algorithm , 2009, 2009 International Conference on Management and Service Science.

[32]  Amrit Pal Singh,et al.  Evaluation performance study of Firefly algorithm, particle swarm optimization and artificial bee colony algorithm for non-linear mathematical optimization functions , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[33]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[34]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.