A semantic approach to improve neighborhood formation in collaborative recommender systems

Martín-Vicente, Manuela I.; Gil‐Solla, Alberto; Ramos‐Cabrer, Manuel; Pazos‐Arias, José J.; Blanco‐Fernández, Yolanda; López‐Nores, Martín

doi:10.1016/j.eswa.2014.06.038

Cited by 34 publications

(25 citation statements)

References 27 publications

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“…The collaborative recommender system reported by Martín-Vicente et al uses semantic reasoning to address the matrix sparsity problem and improve the neighbour selection. User similarity is calculated using the relations and the number of hops between concepts represented in the system's ontology [9]. While this proposal determines similarity between users at the concept level, we choose the nearest neighbours by calculating the similarity between users at the (semantically enriched feature) term level.…”

Section: Semantic Enrichmentmentioning

confidence: 99%

Collaborative Filtering with Semantic Neighbour Discovery

Veloso

Malheiro

Burguillo

2016

Lecture Notes in Computer Science

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Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items-the subset of items co-rated by both users-typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process-a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.

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Section: Semantic Enrichmentmentioning

confidence: 99%

Collaborative Filtering with Semantic Neighbour Discovery

Veloso

Malheiro

Burguillo

2016

Lecture Notes in Computer Science

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“…The exception are the approaches that use inference engines [9,14]. Recent approaches [17,18] manage to handle large ontologies while exploiting relationships along the entire hierarchy.…”

Section: Introductionmentioning

confidence: 99%

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Sheridan¹,

Onsjö

Becerra

et al. 2019

Future Internet

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Collaborative filtering based recommender systems have been extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain when items mostly belong to the same category and are mostly alike (e.g. television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN algorithm. As study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We observed that our proposed method showed to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

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“…Some research use tag information for solving coldstart problem and achieve good results [10]. Recently, some leading researchers have paid attention to items' semantic similarities [11], [12], but the effectiveness is unremarkable.…”

Section: Introductionmentioning

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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A semantic approach to improve neighborhood formation in collaborative recommender systems

Cited by 34 publications

References 27 publications

Collaborative Filtering with Semantic Neighbour Discovery

Collaborative Filtering with Semantic Neighbour Discovery

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

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

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