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This paper focuses on developing effective and efficient algorithms for top-N recommender systems

SLIM: Sparse Linear Methods for Top-N Recommender Systems

ICDM, pp.497-506, (2011)

Cited by: 341|Views257
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Abstract

This paper focuses on developing effective and efficient algorithms for top-N recommender systems. A novel Sparse Linear Method (SLIM) is proposed, which generates top-N recommendations by aggregating from user purchase/rating profiles. A sparse aggregation coefficient matrix W is learned from SLIM by solving an `1-norm and `2-norm regula...More

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Introduction
  • The emergence and fast growth of E-commerce have significantly changed people’s traditional perspective on purchasing products by providing huge amounts of products and detailed product information, making online transactions much easier.
  • Given the user purchase/rating profiles, recommending a ranked list of items for the user so as to encourage additional purchases has the most application scenarios.
  • This leads to the widely used top-N recommender systems.
  • Among the neighborhood-based methods, those based on item neighborhoods can generate recommendations very fast, but they achieve this with a sacrifice on recommendation quality.
  • Model-based methods, those based on latent factor models incur a higher cost while generating recommendations, but the quality of these recommendations is higher, and they have been shown to achieve the best performance especially on large recommendation tasks
Highlights
  • The emergence and fast growth of E-commerce have significantly changed people’s traditional perspective on purchasing products by providing huge amounts of products and detailed product information, making online transactions much easier
  • We propose a novel Sparse LInear Method (SLIM) for top-N recommendation that is able to make highquality recommendations fast
  • We propose a Sparse LInear Method (SLIM) to do top-N recommendation
  • We evaluated the performance of Sparse LInear Method methods on eight different real datasets whose characteristics are shown in Table I
  • We evaluated Sparse LInear Method-b, Pure Singular-Value-Decomposition-based-b, Weighted Regularized Matrix Factorization-b and be well adopted for item knn method-b on the four datasets, for which the models are still learned from binary user-item purchase matrix but the recommendations are evaluated based on ratings
  • We proposed a sparse linear method for topN recommendation, which is able to generate high-quality top-N recommendations fast
Results
  • The authors present the performance of SLIM methods and compare them with other popular top-N recommendation methods.
  • In the first set of experiments, all the top-N recommendation methods use binary user-item purchase information during learning, and all the methods are appended by -b to indicate binary data used (e.g., SLIM-b) if there is confusion.
  • In the second set of experiments, all the top-N recommendation methods use user-item rating information during learning, and correspondingly they are appended by -r if there is confusion.
  • The authors only report the performance corresponding to the parameters that lead to the best results
Conclusion
  • A. Observed Data vs Missing Data.
  • The entries with “0” value can be ambiguous
  • They may either represent that the users will never purchase the items, the users may purchase the items but have not done so, or the authors do not know if the users have purchased the items or not or if they will.
  • Differentiation of observed data and missing data in Equation 4 is under development
Tables
  • Table1: The Datasets Used in Evaluation dataset #users #items
  • Table2: Comparison of Top-N Recommendation Algorithms ccard
  • Table3: Performance Difference on Top-N Recommendations dataset
  • Table4: Performance on the Long Tail of ML10M
Download tables as Excel
Related work
  • Top-N recommender systems are used in E-commerce applications to recommend size-N ranked lists of items that users may like the most, and they have been intensively studied during the last few years. The methods for top-N recommendation can be broadly classified into two categories. The first category is the neighborhood-based collaborative filtering methods [2]. For a certain user, user-based k-nearest-neighbor (userkNN) collaborative filtering methods first identify a set of similar users, and then recommend top-N items based on what items those similar users have purchased. Similarly, itembased k-nearest-neighbor (itemkNN) collaborative filtering methods first identify a set of similar items for each of the items that the user has purchased, and then recommend top-N items based on those similar items. The user/item similarity is calculated from user-item purchase/rating matrix in a collaborative filtering fashion with some similarity measures (e.g., Pearson correlation, cosine similarity) applied. One advantage of the item-based methods is that they are efficient to generate recommendations due to the fact that the item neighborhood is sparse. However, they suffer from low accuracy since there is essentially no knowledge learned about item characteristics so as to produce accurate top-N recommendations.
Funding
  • This work was supported in part by NSF (IIS-0905220, OCI-1048018, and IOS-0820730) and the Digital Technology Center at the University of Minnesota
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