Collaborative Recommendations Using Bayesian Networks and Linguistic Modelling

Campos, Luis M. de; Fernández‐Luna, Juan M.; Huete, Juan F.

doi:10.1007/978-3-540-69731-2_111

Cited by 1 publication

(1 citation statement)

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“…Rating of that group on the test item is output as the prediction. Model based algorithms using Bayesian network [4], [17], aspect models [7], [9], random walk models [19] are discussed in literature. Methods that combine both memory-based and model-based approaches for rating prediction are also suggested [16], [15].…”

Section: Introductionmentioning

confidence: 99%

Aggregating preference graphs for collaborative rating prediction

Desarkar

Sarkar

Mitra

2010

Proceedings of the Fourth ACM Conference on Recommender Systems

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Collaborative filtering is a widely used technique for rating prediction in recommender systems. Memory based collaborative filtering algorithms assign weights to the users to capture similarities between them. The weighted average of similar users' ratings for the test item is output as prediction. We propose a memory based algorithm that is markedly different from the existing approaches. We use preference relations instead of absolute ratings for similarity calculations, as preference relations between items are generally more consistent than ratings across like-minded users. Each user's ratings are viewed as a preference graph. Similarity weights are learned using an iterative method motivated by online learning. These weights are used to create an aggregate preference graph. Ratings are inferred to maximally agree with this aggregate graph. The use of preference relations allows the rating of an item to be influenced by other items, which is not the case in the weighted-average approaches of the existing techniques. This is very effective when the data is sparse, specially for the items rated by few users. Our experiments show that the our method outperforms other methods in the sparse regions. However, for dense regions, sometimes our results are comparable to the competing approaches, and sometimes worse.

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Section: Introductionmentioning

confidence: 99%