English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

User reviews contain rich semantics towards the preference of users tofeatures of items. Recently, many deep learning based solutions have beenproposed by exploiting reviews for recommendation. The attention mechanism ismainly adopted in these works to identify words or aspects that are importantfor rating prediction. However, it is still hard to understand whether a userlikes or dislikes an aspect of an item according to what viewpoint the userholds and to what extent, without examining the review details. Here, weconsider a pair of a viewpoint held by a user and an aspect of an item as alogic unit. Reasoning a rating behavior by discovering the informative logicunits from the reviews and resolving their corresponding sentiments couldenable a better rating prediction with explanation. To this end, in this paper,we propose a capsule network based model for rating prediction with userreviews, named CARP. For each user-item pair, CARP is devised to extract theinformative logic units from the reviews and infer their correspondingsentiments. The model firstly extracts the viewpoints and aspects from the userand item review documents respectively. Then we derive the representation ofeach logic unit based on its constituent viewpoint and aspect. A sentimentcapsule architecture with a novel Routing by Bi-Agreement mechanism is proposedto identify the informative logic unit and the sentiment based representationsin user-item level for rating prediction. Extensive experiments are conductedover seven real-world datasets with diverse characteristics. Our resultsdemonstrate that the proposed CARP obtains substantial performance gain overrecently proposed state-of-the-art models in terms of prediction accuracy.Further analysis shows that our model can successfully discover theinterpretable reasons at a finer level of granularity.

A Capsule Network for Recommendation and Explaining What You Like and Dislike