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A random walk method for alleviating the sparsity problem in collaborative filtering

Published: 23 October 2008 Publication History

Abstract

Collaborative Filtering is one of the most widely used approaches in recommendation systems which predicts user preferences by learning past user-item relationships. In recent years, item-oriented collaborative filtering methods came into prominence as they are more scalable compared to user-oriented methods. Item-oriented methods discover item-item relationships from the training data and use these relations to compute predictions. In this paper, we propose a novel item-oriented algorithm, Random Walk Recommender, that first infers transition probabilities between items based on their similarities and models finite length random walks on the item space to compute predictions. This method is especially useful when training data is less than plentiful, namely when typical similarity measures fail to capture actual relationships between items. Aside from the proposed prediction algorithm, the final transition probability matrix computed in one of the intermediate steps can be used as an item similarity matrix in typical item-oriented approaches. Thus, this paper suggests a method to enhance similarity matrices under sparse data as well. Experiments on MovieLens data show that Random Walk Recommender algorithm outperforms two other item-oriented methods in different sparsity levels while having the best performance difference in sparse datasets.

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      cover image ACM Conferences
      RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
      October 2008
      348 pages
      ISBN:9781605580937
      DOI:10.1145/1454008
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      Published: 23 October 2008

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      Author Tags

      1. collaborative filtering
      2. random walk
      3. sparsity

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      RecSys08: ACM Conference on Recommender Systems
      October 23 - 25, 2008
      Lausanne, Switzerland

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      • (2023)Matrix Completion With Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR SamplingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.326118545:8(10100-10113)Online publication date: Aug-2023
      • (2022)Foundational Recommender Systems for BusinessEncyclopedia of Data Science and Machine Learning10.4018/978-1-7998-9220-5.ch167(2799-2816)Online publication date: 14-Oct-2022
      • (2022)Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation SystemApplied Sciences10.3390/app12221168612:22(11686)Online publication date: 17-Nov-2022
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