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User preferences in recommendation algorithms: the influence of user diversity, trust, and product category on privacy perceptions in recommender algorithms

Published:27 September 2018Publication History

ABSTRACT

The use of recommendation systems is widespread in online commerce. Depending on the algorithm that is used in the recommender system different types of data are recorded from user interactions. Typically, better recommendations are achieved when more detailed data about the user and product is available. However, users are often unaware of what data is stored and how it is used in recommendation. In a survey study with 197 participants we introduced different recommendation techniques (collaborative filtering, content-based recommendation, trust-based and social recommendation) to the users and asked participants to rate what type of algorithm should be used for what type of product category (books, mobile phones, contraceptives). We found different patterns of preferences for different product categories. The more sensitive the product the higher the preference for content-based filtering approaches that could work without storing personal data. Trust-based and social approaches utilizing data from social media were generally rejected.

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          cover image ACM Conferences
          RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
          September 2018
          600 pages
          ISBN:9781450359016
          DOI:10.1145/3240323

          Copyright © 2018 ACM

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          Publication History

          • Published: 27 September 2018

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          RecSys '18 Paper Acceptance Rate32of181submissions,18%Overall Acceptance Rate254of1,295submissions,20%

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          18th ACM Conference on Recommender Systems
          October 14 - 18, 2024
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