Tinderbook: Fall in Love with Culture

Palumbo, Enrico; Buzio, Alberto; Gaiardo, Andrea; Rizzo, Giuseppe; Troncy, Raphaël; Baralis, Elena

doi:10.1007/978-3-030-21348-0_38

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…Other data collection platforms. Our data collection application is inspired by Tinderbook [23], which provides book recommendations based on few binary ratings provided by the user. Similar to MindReader, Tinderbook (i) exploits an extension of state-of-the-art KG embedding methods, and (ii) relies on an existing knowledge base (DBpedia) to obtain book information.…”

Section: Related Workmentioning

confidence: 99%

MindReader

Brams

Jakobsen

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Knowledge Graphs (KGs) have been integrated in several models of recommendation to augment the informational value of an item by means of its related entities in the graph. Yet, existing datasets only provide explicit ratings on items and no information is provided about users' opinions of other (non-recommendable) entities. To overcome this limitation, we introduce a new dataset, called the MindReader dataset, providing explicit user ratings both for items and for KG entities. In this first version, the MindReader dataset provides more than 102 thousands explicit ratings collected from 1,174 real users on both items and entities from a KG in the movie domain. This dataset has been collected through an online interview application that we also release as open source. As a demonstration of the importance of this new dataset, we present a comparative study of the effect of the inclusion of ratings on non-item KG entities in a variety of state-of-the-art recommendation models. In particular, we show that most models, whether designed specifically for graph data or not, see improvements in recommendation quality when trained on explicit non-item ratings. Moreover, for some models, we show that non-item ratings can effectively replace item ratings without loss of recommendation quality. This finding, in addition to an observed greater familiarity from users towards certain descriptive entities than movies, motivates the use of KG entities for both warm and cold-start recommendations.

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Section: Related Workmentioning

confidence: 99%

MindReader

Brams

Jakobsen

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…A recommender system that generates item similarities could use contextual information about items, or use textual information to generate items similar to the item in consideration [1]. Embeddings could be generated for products using graph-based approaches [2][3] or using word2vec [4]. The drawback of these approaches is that in a guest-based recommender system, it is difficult to scale them to millions of users and items as it would make the parameter space of a graph or a neural network too large in a shared memory system, especially if the dimensionality is large.…”

Section: Introductionmentioning

confidence: 99%

Parallel Taxonomy Discovery

Shah¹

2020

Preprint

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<p>Recommender systems aim to personalize the shopping experience of a user by suggesting related products, or products that are found to be in the general interests of the user. The information available for users and products is heterogenous, and many systems use one or some of the information. The information available include the user's interactions history with the products and categories, textual information of the products, a hierarchical classification of the products into a taxonomy, user interests based on a questionnaire, the demographics of a user, inferred interests based on product reviews given by a user, interests based on the physical location of a user and so on. Taxonomy discovery for personalized recommendation is work published in 2014 which uses the first three information sources { the user's interaction history, textual information of the products and optionally, an existing taxonomy of the products. In this paper, we describe a parallel implementation of this approach on Apache Spark and discuss the modifications to the algorithm in order to scale it to several hundreds of thousands of users with a large inventory of products at Target corporation. We run experiments on a sample of users and provide results including some sample recommendations generated by our parallel algorithm.</p>

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Tinderbook: Fall in Love with Culture

Cited by 2 publications

References 20 publications

MindReader

MindReader

Parallel Taxonomy Discovery

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