Gaze Prediction for Recommender Systems
Gaze Prediction for Recommender Systems
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DOI：
10.1145/2959100.2959150
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发表时间：
2016-09
期刊：
影响因子：
--
通讯作者：
Qian Zhao;Shuo Chang;F. M. Harper;J. Konstan
中科院分区：
文献类型：
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作者：
Qian Zhao;Shuo Chang;F. M. Harper;J. Konstan
当用户浏览推荐系统时，他们系统地考虑或跳过大部分显示的内容。很明显，这些眼睛注视模式包含了关于这些用户偏好的丰富信号。然而，由于眼睛跟踪数据对大多数推荐系统不可用，这些信号没有被广泛地纳入个性化模型。在这项工作中，我们表明，它是可以预测的目光相结合，容易收集的用户浏览数据与眼动跟踪数据，从一个基于网格的推荐界面的少数用户。我们的技术能够利用少量的眼动跟踪数据来推断其他用户的注视模式。我们在MovieLens --一个在线电影推荐系统中评估我们的预测模型。我们的研究结果表明，与仅使用浏览数据相比，合并来自少量用户的眼动跟踪数据显着提高了准确性，即使眼动跟踪用户与测试用户不同（例如，在预测用户是否会注视某个项目时，AUC=0.823 vs. 0.693）。我们还证明了隐马尔可夫模型（HHALF）可以应用于这种设置;它们在预测固定概率和通过贝叶斯推断捕获界面规律性方面优于线性模型（AUC=0.823 vs. 0.757）。
As users browse a recommender system, they systematically consider or skip over much of the displayed content. It seems obvious that these eye gaze patterns contain a rich signal concerning these users' preferences. However, because eye tracking data is not available to most recommender systems, these signals are not widely incorporated into personalization models. In this work, we show that it is possible to predict gaze by combining easily-collected user browsing data with eye tracking data from a small number of users in a grid-based recommender interface. Our technique is able to leverage a small amount of eye tracking data to infer gaze patterns for other users. We evaluate our prediction models in MovieLens -- an online movie recommender system. Our results show that incorporating eye tracking data from a small number of users significantly boosts accuracy as compared with only using browsing data, even though the eye-tracked users are different from the testing users (e.g. AUC=0.823 vs. 0.693 in predicting whether a user will fixate on an item). We also demonstrate that Hidden Markov Models (HMMs) can be applied in this setting; they are better than linear models in predicting fixation probability and capturing the interface regularity through Bayesian inference (AUC=0.823 vs. 0.757).