Using the Context of User Feedback in Recommender Systems
This work addresses the challenge of making accurate recommendations for small to medium e-commerce portals where only implicit feedback is available, though it appears incremental as it builds on existing implicit feedback methods by adding context.
The paper tackled the problem of improving recommendations on e-commerce sites lacking explicit feedback by modeling and leveraging contextual features from user feedback, showing that using presentation context enhanced purchase prediction and recommendation tasks in experiments with real users from a Czech travel agency website.
Our work is generally focused on recommending for small or medium-sized e-commerce portals, where explicit feedback is absent and thus the usage of implicit feedback is necessary. Nonetheless, for some implicit feedback features, the presentation context may be of high importance. In this paper, we present a model of relevant contextual features affecting user feedback, propose methods leveraging those features, publish a dataset of real e-commerce users containing multiple user feedback indicators as well as its context and finally present results of purchase prediction and recommendation experiments. Off-line experiments with real users of a Czech travel agency website corroborated the importance of leveraging presentation context in both purchase prediction and recommendation tasks.