A Novel Hierarchical Approach to Ranking-Based Collaborative Filtering

Νικολακόπουλος, Αθανάσιος Ν.; Kouneli, Marianna A.; Garofalakis, John

doi:10.1007/978-3-642-41016-1_6

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…Generally speaking, a recommender system is associated with items that have indirect connections [22] generated from their natural hierarchical structure, by which users can be easily acquainted with different kinds of items. For a user, his/her rating not only expresses his/her direct preference about a certain item, but it also provides a hint about his/herinterest in related items.…”

Section: Indirect Hierarchical Structure Similarity Refinementmentioning

confidence: 99%

A Collaborative Filtering Recommendation Algorithm Based on Hierarchical Structure and Time Awareness

Guo

Tang

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYUser-based and item-based collaborative filtering (CF) are two of the most important and popular techniques in recommender systems. Although they are widely used, there are still some limitations, such as not being well adapted to the sparsity of data sets, failure to consider the hierarchical structure of the items, and changes in users' interests when calculating the similarity of items. To overcome these shortcomings, we propose an evolutionary approach based on hierarchical structure for dynamic recommendation system named Hierarchical Temporal Collaborative Filtering (HTCF). The main contribution of the paper is displayed in the following two aspects. One is the exploration of hierarchical structure between items to improve similarity, and the other is the improvement of the prediction accuracy by utilizing a time weight function. A unique feature of our method is that it selects neighbors mainly based on hierarchical structure between items, which is more reliable than co-rated items utilized in traditional CF. To the best of our knowledge, there is little previous work on researching CF algorithm by combining object implicit or latent object-structure relations. The experimental results show that our method outperforms several current recommendation algorithms on recommendation accuracy (in terms of MAE).

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Section: Indirect Hierarchical Structure Similarity Refinementmentioning

confidence: 99%