Diversified and scalable service recommendation with accuracy guarantee

Lina Wang, Xuyun Zhang, Tian Wang, Shaohua Wan, Gautam Srivastava, Shaoning Pang, Lianyong Qi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user-item rating data decreases the recommendation efficiency significantly as a recommender system needs to analyze all the rating data when searching for similar neighbors or similar items. In this situation, users' requirements on quick response may not be met. Second, in neighbor-based CF methods, more attention is paid to the recommendation accuracy while other key indicators of recommendation performances are often ignored, i.e., recommendation diversity (RD), which probably produces similar or redundant items in the recommended list and decreases users' satisfaction. Considering these issues, a diversified and scalable recommendation method (called DR_LT) based on locality-sensitive hashing and cover tree is proposed in this article, where the item topic information is used to optimize the final recommended list. We show the effectiveness of our proposed method through a set of experiments on MovieLens data set that clearly shows the feasibility of our proposal in terms of item recommendation accuracy, diversity, and scalability.

Original languageEnglish
Pages (from-to)1182-1193
Number of pages12
JournalIEEE Transactions on Computational Social Systems
Issue number5
Publication statusPublished - Oct 2021


  • Recommender systems
  • Scalability
  • Proposals
  • Collaboration
  • Electronic mail
  • Information services
  • Computational modeling
  • Accuracy
  • collaborative filtering (CF)
  • diversity
  • recommendation
  • scalability


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