On online content platforms, users often aim to explore the catalog and discover new, personalized content through exploratory searches-such as "new releases for me." Traditional search systems, which prioritize lexical and semantic matching over personalized retrieval, have historically struggled to support this type of intent.In contrast, recommendation services that leverage user-item and item-item signals tend to be more effective for addressing exploratory queries.Agentic technologies offer a promising opportunity to enhance exploratory search by harnessing large language models (LLMs) to interpret complex query intents and route them to the most suitable downstream services.However, deploying such

Spotify is a peer-assisted music streaming service that has gained worldwide popularity in the past few years. Until now, little has been published about user behavior in such services. In this paper, we study the user behavior in Spotify by analyzing a massive dataset collected between 2010 and 2011. Firstly, we investigate the system dynamics including session arrival patterns, playback arrival patterns, and daily variation of session length. Secondly, we analyze individual user behavior on both multiple and single devices. Our analysis reveals the favorite times of day for Spotify users. We also show the correlations between both the length and the downtime of successive user sessions on single devices. In particular, we conduct the first analysis of the device-switching behavior of a massive user base.

We prove a quantitative version of the Gibbard-Satterthwaite theorem. We show that a uniformly chosen voter profile for a neutral social choice function $f$ of $q \geq 4$ alternatives and $n$ voters will be manipulable with probability at least $10^{-4} \eps^2 n^{-3} q^{-30}$, where $\eps$ is the minimal statistical distance between $f$ and the family of dictator functions. Our results extend those of Fried gut et al, which were obtained for the case of $3$ alternatives, and imply that the approach of masking manipulations behind computational hardness cannot hide manipulations completely. Our proof is geometric. More specifically it extends the method of canonical paths to show that the measure of the profiles that lie on the interface of $3$ or more outcomes is large. To the best of our knowledge our result is the first isoperimetric result to establish interface of more than two bodies.