Immune-Inspired Collaborative Filtering Technology for Rating-Based Recommendation System

A novel technology inspired by the adaptive and self-organizing immune nature is applied to the task of rating-based recommendation technology. Unlike present vector-space model, user\´s multiple interest model is introduced to be the representation of antigen and antibody. The artificial immune networks model, which is a type of competitive learning algorithm is capable of extracting relevant features contained in antigens, useful predictions and recommendations are made from the memory antibody cells which represent an "internal image" of the antigens. It provides better recommendation quality owing to solving to data sparse problem. Experimental results indicate the effectiveness and wide applicability. The advantages are expected to be its ease of adaptation to the dynamic environment.