APMDI-CF: An Effective and Efficient Recommendation Algorithm for Online Users

• Ya-Jun Leng,
• Zhi Wang,
• Dan Peng,
• Huan Zhang,

Vol. 17, No. 11, November 30, 2023
10.3837/tiis.2023.11.008, Download Paper (Free):

• Recommendation System
• Collaborative Filtering
• Affinity propagation clustering
• Mean deviation method

Abstract

Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user’s unrated items, mean deviation method is used to impute values for neighbors’ missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality.

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