论文标题
一种自适应混合主动学习策略，并在协作过滤中免费评级
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering
论文作者
论文摘要
推荐系统是信息检索方法，可以预测用户偏好对个性化服务的偏好。这些系统使用用户提供的反馈和评级来对用户的行为进行建模并生成建议。通常，评级非常稀疏，即，每个用户的评分只有一小部分。为了解决此问题并提高性能，可以使用主动学习策略来选择要评估的最有用的项目。这种评级启发过程丰富了通过信息丰富的评分的交互作用矩阵，因此有助于推荐系统更好地对用户的偏好进行建模。在本文中，我们评估了各种非个人化和个性化的评级启发策略。我们还提出了一种混合策略，该策略可适应地结合了一个非人性化和个性化策略。此外，我们提出了一项新程序，以根据项目的附带信息获得免费评级。我们在Movielens数据集上评估了这些想法。实验表明，我们提出的混合策略优于文献中的策略。我们还建议获得自由评级的程度，以进一步提高性能以及用户体验。
Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to model the behavior of users and to generate recommendations. Typically, the ratings are quite sparse, i.e., only a small fraction of items are rated by each user. To address this issue and enhance the performance, active learning strategies can be used to select the most informative items to be rated. This rating elicitation procedure enriches the interaction matrix with informative ratings and therefore assists the recommender system to better model the preferences of the users. In this paper, we evaluate various non-personalized and personalized rating elicitation strategies. We also propose a hybrid strategy that adaptively combines a non-personalized and a personalized strategy. Furthermore, we propose a new procedure to obtain free ratings based on the side information of the items. We evaluate these ideas on the MovieLens dataset. The experiments reveal that our proposed hybrid strategy outperforms the strategies from the literature. We also propose the extent to which free ratings are obtained, improving further the performance and also the user experience.
