6,478 research outputs found
NRPA: Neural Recommendation with Personalized Attention
Existing review-based recommendation methods usually use the same model to
learn the representations of all users/items from reviews posted by users
towards items. However, different users have different preference and different
items have different characteristics. Thus, the same word or similar reviews
may have different informativeness for different users and items. In this paper
we propose a neural recommendation approach with personalized attention to
learn personalized representations of users and items from reviews. We use a
review encoder to learn representations of reviews from words, and a user/item
encoder to learn representations of users or items from reviews. We propose a
personalized attention model, and apply it to both review and user/item
encoders to select different important words and reviews for different
users/items. Experiments on five datasets validate our approach can effectively
improve the performance of neural recommendation.Comment: 4 pages, 4 figure
Attentive Aspect Modeling for Review-aware Recommendation
In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.Comment: Camera-ready manuscript for TOI
Behavior-Driven Model Design: A Deep Learning Recommendation Model Jointing Users and Products Reviews
Data-driven is widely mentioned, but the data is generated by user behavior. Our work aims to utilize a behavior-driven model design pattern to improve accuracy and provide explanations in review-based recommendations. Review-based recommendation introduces review text to overcome the sparseness and unexplainably of rating or scores-based model. Driven by users rating behavior and human cognitive abilities, we proposed a deep learning recommendation model jointing users and products reviews (DLRM-UPR) to learn user preferences and product characteristics adaptively. The DLRM-UPR consists of word, text, and context co-attention layers considering the interaction between each user-product-context pair. Extensive experiments on real datasets demonstrate that DLRM-UPR outperforms existing state-of-the-art models. In addition, the relevant information in the reviews and the suggestion for improving the user experience can be highlighted to explain the recommendation results
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