Tuning Personalized PageRank for Semantics-Aware Recommendations Based on Linked Open Data (original) (raw)
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In this paper we compare several techniques to automatically feed a graph-based recommender system with features extracted from the Linked Open Data (LOD) cloud. Specifically, we investigated whether the integration of LOD-based features can improve the e↵ectiveness of a graph-based recommender system and to what extent the choice of the features selection technique can influence the behavior of the algorithm by endogenously inducing a higher accuracy or a higher diversity. The experimental evaluation showed a clear correlation between the choice of the feature selection technique and the ability of the algorithm to maximize a specific evaluation metric. Moreover, our algorithm fed with LODbased features was able to overcome several state-of-the-art baselines: this confirmed the e↵ectiveness of our approach and suggested to further investigate this research line.
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We propose LODify, a hybrid recommendation method which measures the semantic similarity of items or resources of interest and combines this with user ratings to make recommendations across diverse domains. The semantic similarity metric draws on information theory and computes the similarity of items based on the information content of their shared characteristics. Detailed semantic analysis of items, considering the special characteristics of Linked Data represented using various kinds of relations, incorporating the relative importance of each relation into the similarity measurement, and successful handling of the item cold-start problem are among the key benefits of the presented approach. We demonstrate how this approach can be successfully applied to provide recommendations and to predict user ratings. 1 LODify and its Innovation Linking Open Data (LOD) project is one of the successful initiatives of the Web of Data which aims to publish and link public datasets in a wide var...
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Semantics-aware recommendation engines have emerged as a new family of systems able to exploit the semantics encoded in unstructured and structured information sources to provide better results in terms of accuracy, diversity and novelty as well as to foster the provisioning of new services such as explanation. In the rising of these new recommender systems, an important role has been played by Linked Data (LD). However, as Linked Data is often very rich and contains many information that may result irrelevant and noisy, an initial step of feature selection may be required in order to select the most meaningful portion of the original dataset. Many approaches have been proposed in the literature for feature selection that exploit different statistical dimensions of the original data. In this paper we investigate the role of the semantics encoded in an ontological hierarchy via schema-summarization when exploited to select the most relevant properties for a recommendation task.
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In this paper, we present our work in progress on u si g LOD data to enhance recommending on existing e-commerce site s. We imagine a situation of e-commerce website employing contentbased or hybrid recommendation. Such recommending a lgorithms need relevant object attributes to produce useful recommendations. However, on some domains, usable a ttributes may be difficult to fill in manually and yet access ible from LOD cloud. For our pilot study, we selected the domain of seco ndhand bookshops, where recommending is extraordinary diff icult because of high ratio of objects/users, lack of sig nificant attributes and small number of the same items in stock. Those difficulties prevents us from successfully apply both collaborat ive and common content based recommenders. We have queried Cz ch language mutation of DBPedia in order to receive ad ditional information about objects (books) and use them as B oolean attributes for hybrid matrix factorization method. Our approach is general an...
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