Aspect Based Recommendations
Published 4 August 2017
Konstantin Bauman, Bing Liu, Alexander Tuzhilin
Citations184
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A recommendation technique that not only can recommend items of interest to the user as traditional recommendation systems do but also specific aspects of consumption of the items to further enhance the user experience with those items.
Abstract
In this paper, we propose a recommendation technique that not only can recommend items of interest to the user as traditional recommendation systems do but also specific aspects of consumption of the items to further enhance the user experience with those items. For example, it can recommend the user to go to a specific restaurant (item) and also order some specific foods there, e.g., seafood (an aspect of consumption). Our method is called Sentiment Utility Logistic Model (SULM). As its name suggests, SULM uses sentiment analysis of user reviews. It first predicts the sentiment that the user may have about the item based on what he/she might express about the aspects of the item and then identifies the most valuable aspects of the user's potential experience with that item. Furthermore, the method can recommend items together with those most important aspects over which the user has control and can potentially select them, such as the time to go to a restaurant, e.g. lunch vs. dinner, and what to order there, e.g., seafood. We tested the proposed method on three applications (restaurant, hotel, and beauty & spa) and experimentally showed that those users who followed our recommendations of the most valuable aspects while consuming the items, had better experiences, as defined by the overall rating.
Keywords
Computer ScienceSocial Sciences
ComputerMatrix Factorization Techniques for Recommender Systems
11,675 Citations2009Yehuda Koren, Robert Bell +1 more
As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Recommender Systems Handbook
3,746 Citations2010Francesco Ricci⋆, Kantor, Paul B +2 more
This handbook illustrates how recommender systems can support the user in decision-making, planning and purchasing processes, and works for well known corporations such as Amazon, Google, Microsoft and AT&T.
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
1,453 Citations2008Ruslan Salakhutdinov, Andriy Mnih
This paper presents a fully Bayesian treatment of the Probabilistic Matrix Factorization (PMF) model in which model capacity is controlled automatically by integrating over all model parameters and hyperparameters and shows that Bayesian PMF models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the Netflix dataset.
A holistic lexicon-based approach to opinion mining
1,388 Citations2008Xiaowen Ding, Bing Liu +1 more
This paper proposes a holistic lexicon-based approach to solving the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews by exploiting external evidences and linguistic conventions of natural language expressions.
AI MagazineContext‐Aware Recommender Systems
1,365 Citations2011Gediminas Adomavičius, Bamshad Mobasher +2 more
An overview of the multifaceted notion of context is provided, several approaches for incorporating contextual information in recommendation process are discussed, and the usage of such approaches in several application areas where different types of contexts are exploited are illustrated.
Solving large scale linear prediction problems using stochastic gradient descent algorithms
1,150 Citations2004Tong Zhang
Stochastic gradient descent algorithms on regularized forms of linear prediction methods, related to online algorithms such as perceptron, are studied, and numerical rate of convergence for such algorithms is obtained.
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
779 Citations2014Yongfeng Zhang, Guokun Lai +4 more
The Explicit Factor Model (EFM) is proposed to generate explainable recommendations, meanwhile keep a high prediction accuracy, and online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user's purchasing behavior.
Advances in Collaborative Filtering
754 Citations2010Yehuda Koren, Robert Bell
Latent aspect rating analysis on review text data
659 Citations2010Hongning Wang, Yue Lu +1 more
Empirical experiments show that the proposed latent rating regression model can effectively solve the problem of LARA, and that the detailed analysis of opinions at the level of topical aspects enabled by the proposed model can support a wide range of application tasks, such as aspect opinion summarization, entity ranking based on aspect ratings, and analysis of reviewers rating behavior.
Sentiment Analysis: Mining Opinions, Sentiments, and Emotions
630 Citations2015Bing Liu
IEEE Intelligent SystemsNew Recommendation Techniques for Multicriteria Rating Systems
500 Citations2007Gediminas Adomavičius, YoungOk Kwon
This article proposes several new techniques for extending recommendation technologies to incorporate and leverage multicriteria rating information and improve recommendation accuracy as compared with single-rating recommendation approaches.
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)
454 Citations2014Qiming Diao, Minghui Qiu +4 more
A probabilistic model based on collaborative filtering and topic modeling is proposed that allows it to capture the interest distribution of users and the content distribution for movies; it provides a link between interest and relevance on a per-aspect basis and it allows us to differentiate between positive and negative sentiments on aPer-Aspect basis.
User Modeling and User-Adapted InteractionRecommender systems based on user reviews: the state of the art
405 Citations2015Li Chen, Guanliang Chen +1 more
This article provides a comprehensive overview of how the review elements have been exploited to improve standard content-based recommending, collaborative filtering, and preference-based product ranking techniques and classifies state-of-the-art studies into two principal branches: review-based user profile building and review- based product profile building.
Explaining Recommendations: Design and Evaluation
316 Citations2015Nava Tintarev, Judith Masthoff
This chapter starts by describing how explanations can be affected by how recommendations are presented, and the role the interaction with the recommender system plays, and introduces a number of explanation styles, and how they are related to the underlying algorithms.
Multi-Criteria Recommender Systems
221 Citations2015Gediminas Adomavičius, YoungOk Kwon
This chapter aims to provide an overview of the class of multi-criteria recommender systems, i.e., the category of recommender systems that use multi-criteria preference ratings, and a review of current algorithms that use multi-criteria ratings for calculating predictions and generating recommendations.
Learning to Rank Features for Recommendation over Multiple Categories
158 Citations2016Xu Chen, Zheng Qin +2 more
This paper proposes a novel model called LRPPM-CF, which is able to improve the performance in the tasks of capturing users' interested features and item recommendation by about 17%-24% and 7%-13%, respectively, as compared with several state-of-the-art methods.
IEEE Transactions on Knowledge and Data EngineeringProduct Aspect Ranking and Its Applications
137 Citations2013Zheng-Jun Zha, Jianxing Yu +3 more
A product aspect ranking framework, which automatically identifies the important aspects of products from online consumer reviews, aiming at improving the usability of the numerous reviews, and develops a probabilistic aspect ranking algorithm to infer the importance of aspects.
Context-Aware Recommendation Based On Review Mining
74 Citations2011Negar Hariri, Bamshad Mobasher +2 more
This paper presents a context#aware recommender system that extracts contextual information from a textual description of user current situation and use it in combination with user ratings history to compute a utility function over the set of items.
Contextual Recommendation based on Text Mining
71 Citations2010Yize Li, Jiazhong Nie +4 more
The experimental results demonstrate that 1) IE-based techniques can help create a large scale context data with decent quality from online reviews, at least for restaurant recommendations; 2) context helps recommender systems rank items, however, does not help predict user ratings; 3) simply using context to filter items hurts recommendation performance, while a new probabilistic latent relational model the authors proposed helps.
Knowledge-Based SystemsAspect-based latent factor model by integrating ratings and reviews for recommender system
66 Citations2016Lin Qiu, Sheng Gao +2 more
A novel model, called Aspect-based Latent Factor Model (ALFM) to integrate ratings and review texts via latent factor model, in which by integrating rating matrix, user-review matrix and item-attribute matrix, the user latent factors and item latent factors with word latent factors can be derived.
NeurocomputingCollaborative filtering with weighted opinion aspects
63 Citations2016Chong Yang, Xiaohui Yu +3 more
This work proposes a framework that incorporates both user opinions and preferences on different aspects in an attempt to make a better understanding of a user' behavior and generate a more accurate recommendation.
User-Oriented Context Suggestion
12 Citations2016Yong Zheng, Bamshad Mobasher +1 more
This paper presents a novel recommendation task, "Context Suggestion", whereby the system recommends contexts in which items may be selected and proposes extensions of well-known context-aware recommendation algorithms such as tensor factorization and deviation-based contextual modeling and adapt them as methods to recommend contexts instead of items.