Collaborating trust and item-prediction with ant colony for recommendation

  • Kaleroun A
  • Batra S
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Online Recommenders are information filtering systems which works on the implicit or explicit information provided by the users and Collaborative Filtering is most widely used technique for this. But the accuracy of the recommendation process is greatly affected by the sparsity in user-item matrix. Though, collaborative filtering is one of the most promising techniques, it still suffers from the cold start problem due to which it is unable to give recommendations to new users. It is also vulnerable to many attacks like shilling attack, grey sheep, etc. which severely hamper the recommendation systems. A trust-based approach combining trust and swarm intelligence (ant colony) with collaborative filtering has been proposed. It also uses item-based predictions in the process of generating recommendations. Ant Colony exhibit self organizing and distributed properties due to which it is used in real time and constantly changing environment. Trust is updated continuously using pheromone updating strategy of ant colony thus, making the system more accurate. By combining these approaches, effective system is proposed which provide solutions to the above mentioned problems of collaborative filtering and predict whether the user will like the certain item or not. Results have been validated using dataset of movies which is available online. © 2014 IEEE.

Author-supplied keywords

  • Ant Colony Optimization
  • Collaborative filtering
  • Recommender systems
  • Slope One
  • Trust

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