Transparent, Scrutable and Explainable User Models for Personalized Recommendation

By Krisztian Balog, Filip Radlinski, Shushan Arakelyan. 2019

181 citations recorded by the source corpus.

Most recommender systems base their recommendations on implicit or explicit item-level feedback provided by users. These item ratings are combined into a complex user model, which then predicts the suitability of other items. While effective, such methods have limited scrutability and transparency. For instance, if a user's interests change, then many item ratings would usually need to be modified to significantly shift the user's recommendations. Similarly, explaining how the system characterizes the user is impossible, short of presenting the entire list of known item ratings. In this paper, we present a new set-based recommendation technique that permits the user model to be explicitly presented to users in natural language, empowering users to understand recommendations made and improve the recommendations dynamically. While performing comparably to traditional collaborative filtering techniques in a standard static setting, our approach allows users to efficiently improve recommendations. Further, it makes it easier for the model to be validated and adjusted, building user trust and understanding.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Xu Chen
  2. Yongfeng Zhang
  3. Xiangnan He
  4. Zheng Qin
  5. Tat-seng Chua
  6. Qingyao Ai
  7. Xiang Wang
  8. Hanxiong Chen

In Information Systems

  1. Xu Chen
  2. Yongfeng Zhang
  3. Xiangnan He
  4. Zheng Qin
  5. Tat-seng Chua
  6. Qingyao Ai
  7. Xiang Wang
  8. Hanxiong Chen

In Management Science and Operations Research

  1. Yongfeng Zhang
  2. Xu Chen

Citations in the corpus