Extended Recommendation Framework: Generating the Text of a User Review as a Personalized Summary
This work addresses the need for more informative recommendations in e-commerce or content platforms, though it appears incremental by building on existing extractive summarization methods.
The authors tackled the problem of enhancing rating-based recommender systems by generating personalized review texts as summaries, showing that using both ratings and items improves performance for both rating estimation and summary generation.
We propose to augment rating based recommender systems by providing the user with additional information which might help him in his choice or in the understanding of the recommendation. We consider here as a new task, the generation of personalized reviews associated to items. We use an extractive summary formulation for generating these reviews. We also show that the two information sources, ratings and items could be used both for estimating ratings and for generating summaries, leading to improved performance for each system compared to the use of a single source. Besides these two contributions, we show how a personalized polarity classifier can integrate the rating and textual aspects. Overall, the proposed system offers the user three personalized hints for a recommendation: rating, text and polarity. We evaluate these three components on two datasets using appropriate measures for each task.