Computer Science > Information Retrieval
arXiv:2505.16121 (cs)
[Submitted on 22 May 2025 (v1), last revised 17 Jul 2026 (this version, v3)]
Abstract:Recommender system is one of the most critical technologies for large internet companies such as Amazon and TikTok. Although millions of users use recommender systems globally everyday, and indeed, much data analysis work has been done to improve the technical accuracy of the system, to our limited knowledge, there has been little attention paid to analysis of users' emotion in recommender systems. In this paper, we create a new theory and metrics that could capture users' emotion when they are interacting with recommender systems. We also provide effective and efficient visualization techniques for visualization of users' emotion and its change in the customers' lifetime cycle. In the end, we design a framework for emotion-based recommendation algorithms, illustrated in a straightforward example with experimental results to demonstrate the effectiveness of our new theory.
Submission history
From: Hao Wang [view email]
[v1]
Thu, 22 May 2025 01:54:58 UTC (1,347 KB)
[v2]
Wed, 28 May 2025 02:11:16 UTC (1,347 KB)
[v3]
Fri, 17 Jul 2026 07:52:46 UTC (1,347 KB)
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