Collaborative Filtering with Semantic Neighbour Discovery

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Veloso, B (Author) Other; Malheiro, B (Author) Other; Burguillo, JC (Author) Other

Conference proceedings International

Title: ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2016

Pages: 273-284

15th Ibero-American Conference on Artificial Intelligence (AI)

San Jose, COSTA RICA, NOV 23-25, 2016

Indexing

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Other information

Authenticus ID: P-00M-3XA

DOI: 10.1007/978-3-319-47955-2_23

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12