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Data-driven decision making in critique-based recommenders: from a critique to social media data

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Abstract

In the last decade there have been a large number of proposals in the field of Critique-based Recommenders. Critique-based recommenders are data-driven in their nature since they use a conversational cyclical recommendation process to elicit user feedback. In the literature, the proposals made differ mainly in two aspects: in the source of data and in how this data is analyzed to extract knowledge for providing users with recommendations. In this paper, we propose new algorithms that address these two aspects. Firstly, we propose a new algorithm, called HOR, which integrates several data sources, such as current user preferences (i.e., a critique), product descriptions, previous critiquing sessions by other users, and users’ opinions expressed as ratings on social media web sites. Secondly, we propose adding compatibility and weighting scores to turn user behavior into knowledge to HOR and a previous state-of-the-art approach named HGR to help both algorithms make smarter recommendations. We have evaluated our proposals in two ways: with a simulator and with real users. A comparison of our proposals with state-of-the-art approaches shows that the new recommendation algorithms significantly outperform previous ones.

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Notes

  1. In this work, we use the term user to refer to both an e-commerce customer/shopper and a recommender system user.

  2. In high-risk product domains, the task of locating a desired choice among a large set of options is indeed becoming intimidating for the average customer (Chen and Pu 2012) because there are so many available alternatives.

  3. In complex product spaces, users require a good knowledge of the large amount of characteristics of the products and their relationship with the different available options.

  4. Also referred to as critique-based recommenders in the literature.

  5. A perceptive feature is a feature that provides an immediate and intuitive recognition or appreciation of the qualities of a product. For example, in the SMARTPHONE domain, performance is a perceptive feature that intuitively includes more than one of the technical features of a product (e.g., storage, RAM, or CPU).

  6. To simplify the example, we only show the name of the model of the recommended product ri, but the pair contains all of the features of the product.

  7. This data set was automatically obtained from opinions in the web http://www.gsmarena.com and it is available on demand.

  8. This data set stems from a recent crawl of an online restaurant database for Dublin in the web http://www.tripadvisor.co.uk. Moreover, it has been used in Salem et al. (2014) and was kindly provided by the authors.

  9. The application is available in http://164.77.134.92:8081/WebRec/signin.htm and a video featuring the most important parts of our system is available in https://youtu.be/HTH-9wyTGjU

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Acknowledgements

This research has also received support from the projects SGR-2017-341 and TIN2015-71147-C2-2 from the Spanish Ministry of Science and Innovation.

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Authors and Affiliations

  1. Facultad de Ingeniería y Arquitectura, Universidad Arturo Prat, Avenida Arturo Prat, 2120, Iquique, Chile

    David Contreras

  2. Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585-08007, Barcelona, Spain

    Maria Salamó

  3. Institute of Complex Systems, University of Barcelona, Barcelona, Spain

    Maria Salamó

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  1. David Contreras
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  2. Maria Salamó
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Correspondence to David Contreras.

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Contreras, D., Salamó, M. Data-driven decision making in critique-based recommenders: from a critique to social media data. J Intell Inf Syst 54, 23–44 (2020). https://doi.org/10.1007/s10844-018-0520-9

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