图 书 馆
吸引、厌恶和社会影响下的最优推荐Optimal Recommendations under Attraction, Aversion, and Social Influence |
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| 课程网址: | http://videolectures.net/kdd2014_lu_social_influence/ |
| 主讲教师: | Wei Lu |
| 开课单位: | 不列颠哥伦比亚大学 |
| 开课时间: | 2014-07-10 |
| 课程语种: | 英语 |
| 中文简介: |  人们的兴趣是动态变化的，通常会受到外部因素的影响，例如媒体宣传或朋友采用的趋势。在这项工作中，我们通过动态兴趣级联对兴趣演变进行建模：我们考虑一个场景，其中用户的兴趣可能受到（a）她社交圈中其他用户的兴趣，以及（b）她从推荐人那里收到的建议的影响系统。在后一种情况下，我们通过对过去建议的吸引力或厌恶来模拟用户反应。我们研究了这种兴趣演变过程，以及推荐产生的效用，作为系统推荐策略的函数。我们表明，在稳定状态下，最佳策略可以计算为半定规划 (SDP) 的解决方案。使用用户评分数据集，我们为现实生活数据中存在厌恶和吸引提供了证据，并表明与忽略厌恶和吸引的系统相比，我们的最佳策略可以显着改善推荐。 |
| 课程简介: | 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. |
| 关 键 词: | 兴趣演变; 评分数据集; 半定规划 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-09：zyk |
| 最后编审: | 2021-06-09：zyk |
| 阅读次数: | 43 |