With the rapid development of cloud computing technology, massive services and online information cause information overload. Collaborative Filtering (CF) is one of the most successful and widely used technologies in personalized recommendation system to deal with information overload. However, traditional CF recommendation algorithms go through high time cost and poor real-time performance when dealing with the large-scale behavior data. Moreover, most collaborative recommendation methods mainly focus on improving recommendation accuracy, while ignore privacy preservation. In addition, the recommendation results of traditional CF recommendation algorithms are often too single, which could not meet user's diverse requirements. To solve these problems, this paper proposes a privacy-aware collaborative recommendation algorithm based on local sensitive hash (LSH) and factorization techniques. First, LSH is adopted to determine nearest neighbor set of the target users, where a neighbor matrix for the target user can be generated. The matrix factorization technique is applied in the neighbor matrix to predict the missing ratings. Then the nearest neighbors can be determined based on the predicted ratings. Finally, predictions for the target user are made based on the neighborhood-based CF recommendation model and diversified recommendations are made for the target user. Experimental results show that the proposed algorithm can effectively improve the efficiency of recommendation on the premise of protecting the privacy of users.
|Title of host publication||Proceedings - 2020 IEEE 13th International Conference on Cloud Computing, CLOUD 2020|
|Publisher||IEEE Computer Society|
|Number of pages||8|
|Publication status||Published - Oct 2020|
|Event||13th IEEE International Conference on Cloud Computing, CLOUD 2020 - Virtual, Beijing, China|
Duration: 18 Oct 2020 → 24 Oct 2020
|Name||IEEE International Conference on Cloud Computing, CLOUD|
|Conference||13th IEEE International Conference on Cloud Computing, CLOUD 2020|
|Period||18/10/20 → 24/10/20|
Bibliographical noteFunding Information:
ACKNOWLEDGEMENTS This paper is partially supported by the National Natural Science Foundation of China under Grant No. 61702264, No. 61702263, No. 61761136003, the Fundamental Research Funds for the Central Universities under Grant No. 30918014108, the Postdoctoral Science Foundation of China under Grant No. 2019M651835.
© 2020 IEEE.
- Cloud computing
- Collaborative recommendation
- Local sensitive hash
- Matrix factorization