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Reviews typically contain statements providing argumentative support for a\u00a0given item rating that can be exploited to explain the recommended items in a\u00a0personalized manner. We propose a\u00a0novel method called Aspect-based Transparent Memories (ATM) to model user preferences with respect to relevant aspects and compare them to item properties to predict ratings, and, by the same mechanism, explain why an item is recommended. The ATM architecture consists of two neural memories that can be viewed as arrays of slots for storing information about users and items. The first memory component encodes representations of sentences composed by the target user while the second holds an equivalent representation for the target item based on statements of other users. An offline evaluation was performed with three datasets, showing advantages over two baselines, the well-established Matrix Factorization technique and a\u00a0recent competitive representative of neural attentional recommender techniques.<\/jats:p>","DOI":"10.1007\/s13222-020-00350-y","type":"journal-article","created":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T07:53:04Z","timestamp":1593589984000},"page":"181-187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Leveraging Arguments in User Reviews for Generating and Explaining Recommendations"],"prefix":"10.1007","volume":"20","author":[{"given":"Tim","family":"Donkers","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9603-5272","authenticated-orcid":false,"given":"J\u00fcrgen","family":"Ziegler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,1]]},"reference":[{"key":"350_CR1","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems, ACM","author":"G Askalidis","year":"2016","unstructured":"Askalidis G, Malthouse EC (2016) The value of online customer reviews. 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