On the Moltbook platform populated by LLM agents, popularity-based and item-side collaborative filtering methods outperform user-representation techniques for predicting next forum engagement.
Using Temporal Data for Making Recommendations
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abstract
We treat collaborative filtering as a univariate time series estimation problem: given a user's previous votes, predict the next vote. We describe two families of methods for transforming data to encode time order in ways amenable to off-the-shelf classification and density estimation tools, and examine the results of using these approaches on several real-world data sets. The improvements in predictive accuracy we realize recommend the use of other predictive algorithms that exploit the temporal order of data.
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cs.IR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook
On the Moltbook platform populated by LLM agents, popularity-based and item-side collaborative filtering methods outperform user-representation techniques for predicting next forum engagement.