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A Cognitively Inspired Clustering Approach for Critique-Based Recommenders

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Abstract

The purpose of recommender systems is to support humans in the purchasing decision-making process. Decision-making is a human activity based on cognitive information. In the field of recommender systems, critiquing has been widely applied as an effective approach for obtaining users’ feedback on recommended products. In the last decade, there have been a large number of proposals in the field of critique-based recommenders. These proposals mainly differ in two aspects: in the source of data and in how it is mined to provide the user with recommendations. To date, no approach has mined data using an adaptive clustering algorithm to increase the recommender’s performance. In this paper, we describe how we added a clustering process to a critique-based recommender, thereby adapting the recommendation process and how we defined a cognitive user preference model based on the preferences (i.e., defined by critiques) received by the user. We have developed several proposals based on clustering, whose acronyms are MCP, CUM, CUM-I, and HGR-CUM-I. We compare our proposals with two well-known state-of-the-art approaches: incremental critiquing (IC) and history-guided recommendation (HGR). The results of our experiments showed that using clustering in a critique-based recommender leads to an improvement in their recommendation efficiency, since all the proposals outperform the baseline IC algorithm. Moreover, the performance of the best proposal, HGR-CUM-I, is significantly superior to both the IC and HGR algorithms. Our results indicate that introducing clustering into the critique-based recommender is an appealing option since it enhances overall efficiency, especially with a large data set.

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Notes

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

  2. It is also referred to as critiquing-based recommendation in the literature.

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

  4. Analyzed by means of the Average Session Length, which measures the number of cycles that a user must work through before being presented with their ideal target product.

  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. This data set is available on demand.

  7. This data set has been used in [42] and it was kindly provided by the authors.

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Funding

This study was supported by Spanish Ministry of Science and Innovation (grant number TIN2015-71147-C2-2) and by Agència de Gestió d’Ajuts Universitaris i de Recerca, Generalitat de Catalunya, AGAUR (grant number SGR-2017-341).

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Authors and Affiliations

  1. Universidad Arturo Prat, Avenida Arturo Prat, 2120, Iquique, Chile

    David Contreras

  2. 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

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

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Contreras, D., Salamó, M. A Cognitively Inspired Clustering Approach for Critique-Based Recommenders. Cogn Comput 12, 428–441 (2020). https://doi.org/10.1007/s12559-018-9586-5

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