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Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank

Md Momen Bhuiyan, Donghan Hu, Andrew Jelson, Tanushree Mitra, Sang Won Lee

TL;DR

This study investigates why and how users seek recommendations on the crowdsourced subreddit r/ifyoulikeblank, and how responses unfold, using a large Reddit dataset from 2022 analyzed with qualitative open coding. It identifies a fivefold taxonomy of query characteristics (Artifact, Production/Distribution, Artist, OP context, Additional Information) and analyzes response and interaction patterns to illuminate how recommenders might better support query-by-example, justification, and iterative refinement. The findings highlight opportunities for intelligent, human-in-the-loop recommender designs that expressively capture user needs, reduce reliance on popularity bias, and better accommodate niche tastes. Overall, the work provides design principles and practical considerations for integrating crowdsourced, human-guided preferences into next-generation recommender systems.

Abstract

Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.

Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank

TL;DR

This study investigates why and how users seek recommendations on the crowdsourced subreddit r/ifyoulikeblank, and how responses unfold, using a large Reddit dataset from 2022 analyzed with qualitative open coding. It identifies a fivefold taxonomy of query characteristics (Artifact, Production/Distribution, Artist, OP context, Additional Information) and analyzes response and interaction patterns to illuminate how recommenders might better support query-by-example, justification, and iterative refinement. The findings highlight opportunities for intelligent, human-in-the-loop recommender designs that expressively capture user needs, reduce reliance on popularity bias, and better accommodate niche tastes. Overall, the work provides design principles and practical considerations for integrating crowdsourced, human-guided preferences into next-generation recommender systems.

Abstract

Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.
Paper Structure (66 sections, 3 figures, 6 tables)

This paper contains 66 sections, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Recommender System Taxonomy and Feature Consideration
  4. Popularity Bias Issue during Recommendations Seeking
  5. Methodology
  6. About r/ifyoulikeblank
  7. Data Collection
  8. Sampling for Qualitative Coding and Coding Process
  9. RQ1: Why do people solicit recommendations in r/ifyoulikeblank?
  10. Inadequacy in Other Methods
  11. When It Is Unclear How to Search for Content
  12. Thinking r/ifyoulikeblank users Having Better Expertise than Search Systems
  13. RQ2: How do people seek recommendations in r/ifyoulikeblank?
  14. Query - Artifact
  15. Message/Theme
  16. ...and 51 more sections

Figures (3)

  • Figure 1: Summary statistics for the collected posts and comments
  • Figure 1: Grid of images for an example query.
  • Figure 2: Distribution of user participation in r/ifyoulikeblank. (a) Y No. of user submitting X number of posts (b) Y No. of posts having X number of comments (c) Y No. of user submitting X number of comments. X axis is capped at 10.