Web page recommender systems usually provide users with titles and snippets of recommended pages when the systems present a list of recommendations. Snippets help users judge whether recommended web pages are relevant or not. However, while search engines usually show a text span around a search query as a snippet, web page recommender systems cannot leverage the snippet generation methods used by search engines because the recommender systems have no search queries. Web page recommender systems thus generally use lead sentences, i.e. the first sentences of web pages, as a snippet, but lead sentences are not necessarily relevant to user’s interest. Furthermore since user’s information needs can be different from each other, personalized snippets are desirable to support user’s relevance judgment. Therefore, we propose a new method to generate personalized snippets for web page recommender systems that uses reasons why the web pages are recommended to the user. This use of reasons enables snippets to reflect the interest of the user. Furthermore, since our formulation does not depend on a certain recommender system, our method can be applied to diverse recommender systems. The experimental result on manually created dataset shows that our method is superior to the existing method and generic summarization model in terms of ROUGE-2. In addition, our method achieves comparable performance with the lead method despite that our method restricts itself to sentence selection while the lead method is free to extract a part of a sentence at the end of its snippets.