吸引力、厌恶和社会影响下的最佳建议

Wei Lu, Stratis Ioannidis, Smriti Bhagat, L. Lakshmanan
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引用次数: 24

摘要

人们的兴趣是动态变化的,经常受到外部因素的影响,如媒体推动的趋势或他们的朋友所采用的。在这项工作中,我们通过动态兴趣级联来建模兴趣演变:我们考虑一个场景,其中用户的兴趣可能受到(a)她的社交圈中其他用户的兴趣以及(b)她从推荐系统收到的建议的影响。在后一种情况下,我们通过吸引或厌恶过去的建议来模拟用户的反应。我们研究了这个兴趣演化过程,以及作为系统推荐策略函数的推荐效用。我们证明,在稳态下,最优策略可以计算为半确定规划(SDP)的解。使用用户评分数据集,我们为现实生活数据中厌恶和吸引力的存在提供了证据,并表明我们的最优策略可以显著改善忽略厌恶和吸引力的系统的推荐。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal recommendations under attraction, aversion, and social influence
People's interests are dynamically evolving, often affected by external factors such as trends promoted by the media or adopted by their friends. In this work, we model interest evolution through dynamic interest cascades: we consider a scenario where a user's interests may be affected by (a) the interests of other users in her social circle, as well as (b) suggestions she receives from a recommender system. In the latter case, we model user reactions through either attraction or aversion towards past suggestions. We study this interest evolution process, and the utility accrued by recommendations, as a function of the system's recommendation strategy. We show that, in steady state, the optimal strategy can be computed as the solution of a semi-definite program (SDP). Using datasets of user ratings, we provide evidence for the existence of aversion and attraction in real-life data, and show that our optimal strategy can lead to significantly improved recommendations over systems that ignore aversion and attraction.
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