Exploring Users' Perception of Collaborative Explanation Styles

05/02/2018
by   Ludovik Coba, et al.
0

Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value different collaborative explanation styles following the user-based or item-based paradigm. Furthermore, we explore how the characteristics of these rating summarizations, like the total number of ratings and the mean rating value, influence the decisions of online users. Results, based on a choice-based conjoint experimental design, show that the mean indicator has a higher impact compared to the total number of ratings. Finally, we discuss how these empirical results can serve as an input to developing algorithms that foster items with a, consequently, higher probability of choice based on their rating summarizations or their explainability due to these ratings when ranking recommendations.

READ FULL TEXT
research
05/10/2022

Tensor-based Collaborative Filtering With Smooth Ratings Scale

Conventional collaborative filtering techniques don't take into consider...
research
03/17/2023

Explaining the Performance of Collaborative Filtering Methods With Optimal Data Characteristics

The performance of a Collaborative Filtering (CF) method is based on the...
research
06/20/2012

Collaborative Filtering and the Missing at Random Assumption

Rating prediction is an important application, and a popular research to...
research
05/29/2018

Decision Making of Maximizers and Satisficers Based on Collaborative Explanations

Rating-based summary statistics are ubiquitous in e-commerce, and often ...
research
04/13/2020

A Robust Reputation-based Group Ranking System and its Resistance to Bribery

The spread of online reviews and opinions and its growing influence on p...
research
06/27/2012

Visualization of Collaborative Data

Collaborative data consist of ratings relating two distinct sets of obje...
research
10/20/2018

Attribute-aware Collaborative Filtering: Survey and Classification

Attribute-aware CF models aims at rating prediction given not only the h...

Please sign up or login with your details

Forgot password? Click here to reset