a ranking method based on users' contexts for information recommendation
We propose a ranking method using a Support Vector Machine for information recommendation. By using the SVM, a recommendation method can determine suitable items for a user from enormous item sets. However, it can decide based on just two classes: whether the user likes a thing or not. When there is a large number of recommended items, it is not easy for the user to find the best item by herself. To resolve this issue, it is desirable to rank the items based on the user's preferences. Moreover, the user's preferences change depending on the context. Based on the above problem, we propose a context-aware ranking method for information recommendation. Our method considers a user's context when ranking items. Our method consists of the following two steps: (1) Predicting important feature parameters for the user. (2) Calculating a ranking score of each item in recommendation candidates. In this paper, we describe our method and show experimental results.
ranking information recommendation svm context-awareness