Leveraging clustering to improve collaborative filtering

Mirbakhsh, Nima; Ling, Charles X.

doi:10.1007/s10796-016-9668-4

Cited by 15 publications

(7 citation statements)

References 19 publications

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“…In the second step, for all points i and j such that i = j, the availability a(i, j) is computed by using (12). The availability a(j, j) (also known as self-availability) is computed by using (13).…”

Section: ) Affinity Propagationmentioning

confidence: 99%

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Improving Prediction Accuracy in Neighborhood-Based Collaborative Filtering by Using Local Similarity

et al. 2020

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Neighborhood-based algorithms are some of the most promising memory-based collaborative filtering approaches for recommender systems. Many of these algorithms rely on a global similarity measure to select the most similar neighbors for rating prediction. However, these approaches may fail in capturing some meaningful relationships among users. In the real world, although users can show interest in a wide range of objects, they can express more interest in objects contained in a specific topic, which typically comprises a bulk of closely related objects. In this paper, we propose a local similarity method that has the ability to exploit multiple correlation structures between users who express their preferences for objects that are likely to have similar properties. For this, we use a clustering method to find groups of similar objects. Then we create a user-based similarity model for each cluster, which we named Clusterbased Local Similarity (CBLS) model. Each similarity model relies on rating normalization and resource allocation techniques that are sensitive to the ratings assigned to objects contained in the cluster. We performed experiments using two clustering algorithms (affinity propagation and K-Means) and compared the results with other neighborhood-based collaborative filtering approaches. Our numerical results on three benchmark datasets (MovieLens 100k, MovieLens 1M, and Netflix) demonstrate that the proposed method is competitive and outperforms traditional and state-of-the-art collaborative filtering-based similarity models in terms of accuracy metrics like mean absolute error (MAE) and root-mean-square error (RMSE).

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Section: ) Affinity Propagationmentioning

confidence: 99%

“…Model-based algorithms rely on constructing latent factors models with high generalization capacity to predict unrated objects. Some of the most successful model-based CF techniques include matrix factorization [7]- [9], probabilistic models [10], and clustering-based methods [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Improving Prediction Accuracy in Neighborhood-Based Collaborative Filtering by Using Local Similarity

et al. 2020

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“…These selected values should not be too large or too small [Mirbakhsh and Ling 2014]. We guess different selections of possible categories by changing the number of clusters.…”

Section: Making Single-domain Coarse Matricesmentioning

confidence: 99%

Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information

Mirbakhsh

Ling

2015

ACM Trans. Knowl. Discov. Data

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Making accurate recommendations for cold-start users is a challenging yet important problem in recommendation systems. Including more information from other domains is a natural solution to improve the recommendations. However, most previous work in cross-domain recommendations has focused on improving prediction accuracy with several severe limitations. In this article, we extend our previous work on clustering-based matrix factorization in single domains into cross domains. In addition, we utilize recent results on unobserved ratings. Our new method can more effectively utilize data from auxiliary domains to achieve better recommendations, especially for cold-start users. For example, our method improves the recall to 21% on average for cold-start users, whereas previous methods result in only 15% recall in the cross-domain Amazon dataset. We also observe almost the same improvements in the Epinions dataset. Considering that it is often difficult to make even a small improvement in recommendations, for cold-start users in particular, our result is quite significant. ACM Reference Format:Nima Mirbakhsh and Charles X. Ling. 2015. Improving top-N recommendation for cold-start users via crossdomain information.

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“…Recent work on clustering for RS indicates its popularity as a method for enhancing recommendation quality (Rimaz et al 2019). It is important to note that the majority of the clustering-, similarity-and dimensionality-reduction approaches developed for filtering-based systems or to solve cold-start problems all operate on the user-to-item preferences (or ratings) matrix (Du et al 2017;Felício et al 2016Felício et al , 2017Kluver and Konstan 2014;Mauro and Ardissono 2019;Mirbakhsh and Ling 2018;O'Connor and Herlocker 1999;Sacharidis 2017;Sollenborn and Funk 2002;Shani et al 2007;Wibowo et al 2018). Recently, groupings of users and items have been performed via neural networks-driven text embedding, like word2vec doc2vec, leading to an algorithm capable of grouping users and items via their metadata.…”

Section: Introductionmentioning

confidence: 99%

Improving cold-start recommendations using item-based stereotypes

ALRossais

Kudenko

Yuan

2021

User Model User-Adap Inter

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Recommender systems (RSs) have become key components driving the success of e-commerce and other platforms where revenue and customer satisfaction is dependent on the user’s ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RSs, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold-start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. We propose a set of methodologies for the automatic generation of stereotypes to address the cold-start problem. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. The paper describes how such item-based stereotypes can be evaluated via a series of statistical tests prior to being used for recommendation. The proposed approach improves recommendation quality under a variety of metrics and significantly reduces the dimension of the recommendation model.

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Leveraging clustering to improve collaborative filtering

Cited by 15 publications

References 19 publications

Improving Prediction Accuracy in Neighborhood-Based Collaborative Filtering by Using Local Similarity

Improving Prediction Accuracy in Neighborhood-Based Collaborative Filtering by Using Local Similarity

Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information

Improving cold-start recommendations using item-based stereotypes

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