A Stable Collaborative Filtering Algorithm for Long Tail Recommendation

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As a relatively successful recommendation system, the collaborative filtering recommendation system (CFRS) has been widely used in e-commerce. However, the current CFRS is mainly based on mainstream or popular products to recommending similar items for users and is less efficiency in recommend the so called “Long Tail” products to meet the individual needs of users. Based on the Item-based system filtering recommendation algorithm, this paper proposes a collaborative filtering recommendation algorithm that implements long tail recommendation by using the item rating probability matrix and item rating reliability. Compared with the traditional collaborative filtering algorithm, the experimental result based on MovieLens 1M dataset shows that the proposed algorithm can deal with the data sparsity problem better, and is better for producing recommendation for the long tail products effect, and furthermore, it shows stability to a certain extent in producing recommendations under different situations of data sparseness.