人工智能时代的个性化推荐

People-oriented:Personalized Recommendations in the Era of Artifi cial Intelligence

个性化推荐通过收集和分析用户的行为信息,预测用户的兴趣偏好并进行推荐,通过影响用户的消费行为,从而产生经济效益。个性化推荐历经了基于统计学、基于内容、基于协同过滤、基于社交网络和混合式推荐的发展历程,虽然已取得了一定效果,但是仍然无法令人满意。随着人工智能时代的到来,多学科多领域的融合为个性化推荐提供了新的思路。本文首先回顾并分析了现有个性化推荐的主要方式、存在的问题和实际需求,然后根据管理学和心理学相关理论模型,提出人工智能时代的个性化推荐需要以人为本,关注用户特征,通过构建用户认知模型,评估用户心理抗拒程度,建立不同用户的消费动机模型,建立更全面的推荐评价体系。

Personalized recommendation affects the consumption behavior by collecting and analyzing the user's behavior information,predicting the user's interest and preferences and making recommendations,thereby generating economic benefi ts.Personalized recommendation has gone through the development of statisticsbased,content-based,collaborative fi ltering,social network-based and hybrid recommendation.Although it has achieved certain results,it is still not satisfactory.With the advent of the era of artifi cial intelligence,multiple disciplines and fi elds provide new approach for personalized recommendation.This article fi rst reviews the main methods about personalized recommendation,and analyses problems and potential needs.Then,based on the theoretical models of management and psychology,it discusses that personalized recommendations in the era of artifi cial intelligence need to be people-oriented and focus on user’s psychological state and proposes several ways to improve the eff ectiveness of personalized recommendations.