Diversifying recommendations on sequences of sets
Diversifying recommendations on sequences of sets
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DOI：
10.1007/s00778-022-00740-6
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发表时间：
2022-05
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通讯作者：
Sepideh Nikookar;M. Esfandiari;R. M. Borromeo;Paras Sakharkar;S. Amer-Yahia;Senjuti Basu Roy
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作者：
Sepideh Nikookar;M. Esfandiari;R. M. Borromeo;Paras Sakharkar;S. Amer-Yahia;Senjuti Basu Roy
对一系列项目集（或会话）的多样化推荐可以捕获各种应用。值得注意的示例包括推荐在线音乐播放列表，其中会话是一个频道并且按顺序收听多个频道，或者推荐众包中的任务，其中会话是一组任务并且按顺序完成多个任务会话。项目多样性可以以多种方式定义，例如，作为音乐的流派多样性，或者作为众包的回报函数。参与多个会话的用户可能希望体验会话内和/或会话间的多样性。会话内多样性是基于集合的，而会话间多样性自然是基于序列的。这种新的配方产生了四个双目标问题的目标是最大限度地减少或最大化InterandIntradibrium。我们证明了硬度和开发有效的算法与理论保证。我们在两个真实的数据集上对人类受试者进行的实验表明，我们的多样性公式确实满足了不同的用户需求，并获得了很高的用户满意度。我们在真实的和合成数据上的大规模实验经验性地证明，与基线相比，我们的解决方案满足我们的理论界限并且具有高度可扩展性。
Diversifying recommendations on a sequence of sets (or sessions) of items captures a variety of applications. Notable examples include recommending online music playlists, where a session is a channel and multiple channels are listened to in sequence, or recommending tasks in crowdsourcing, where a session is a set of tasks and multiple task sessions are completed in sequence. Item diversity can be defined in more than one way, e.g., as a genre diversity for music, or as a function of reward in crowdsourcing. A user who engages in multiple sessions may intend to experience diversity within and/or across sessions.Intrasession diversity is set-based, whereasIntersession diversity is naturally sequence-based. This novel formulation gives rise to four bi-objective problems with the goal of minimizing or maximizingInterandIntradiversities. We prove hardness and develop efficient algorithms with theoretical guarantees. Our experiments with human subjects on two real datasets show that our diversity formulations do serve different user needs and yield high user satisfaction. Our large-scale experiments on real and synthetic data empirically demonstrate that our solutions satisfy our theoretical bounds and are highly scalable, compared to baselines.