Opinion Recommendation Using A Neural Model
We present opinion recommendation, a novel task of jointly generating a review with a rating score that a certain user would give to a certain product which is unreviewed by the user, given existing reviews to the product by other users, and the reviews that the user has given to other products. A characteristic of opinion recommendation is the reliance of multiple data sources for multi-task joint learning. We use a single neural network to model users and products, generating customised product representations using a deep memory network, from which customised ratings and reviews are constructed jointly. Results show that our opinion recommendation system gives ratings that are closer to real user ratings on Yelp.com data compared with Yelp{'}s own ratings. our methods give better results compared to several pipelines baselines.
Code (0)
등록된 구현이 없습니다.
Tasks
modelSentiment AnalysisSimilar Papers 제목 키워드 기반
Coevolution of Opinion Dynamics and Recommendation System: Modeling Analysis and Reinforcement Learning Based Manipulation
In this work, we develop an analytical framework that integrates opinion dynamics with a recommendation system. By incorporating elements such as collaborative filtering, we provide a precise characterization of how reco…
Collaborative FilteringRecommendation SystemsThe Feedback Loop Between Recommendation Systems and Reactive Users
Recommendation systems underlie a variety of online platforms. These recommendation systems and their users form a feedback loop, wherein the former aims to maximize user engagement through personalization and the promot…
Recommendation SystemsExplainable Recommendation via Multi-Task Learning in Opinionated Text Data
Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-tas…
Explainable RecommendationMulti-Task LearningThe closed loop between opinion formation and personalised recommendations
In online platforms, recommender systems are responsible for directing users to relevant contents. In order to enhance the users' engagement, recommender systems adapt their output to the reactions of the users, who are …
Recommendation SystemsFineRec:Exploring Fine-grained Sequential Recommendation
Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to captu…
AttributeDiversityLanguage ModelingLanguage Modelling+4