Now showing 1 - 10 of 26
- PublicationConvolutional Matrix Factorization for Recommendation ExplanationIn this paper, we introduce a novel recommendation model, which harnesses a convolutional neural network to mine meaningful information from customer reviews, and integrates it with matrix factorization algorithm seamlessly. It is a valid method to improve the transparency of CF algorithms.
505Scopus© Citations 3
- PublicationUP-TreeRec: Building dynamic user profiles tree for news recommendationOnline News recommendation systemsaim to address the information explosion of news and make personalized recommendation for users. The key problem of personalized news recommendation is to model users’ interests and track their changes. A common way to deal with usermodeling problem is to build user profiles from observed behavior. However, the majority of existing methods make static representation to user profiles and little research has focused on the effective user modeling that could dynamically capture user interests in news topics.To address this problem, in this paper, we propose UP-TreeRec, a news recommendation framework based on user profile tree (UP-Tree), which is a novel framework combining content-based and collaborative filtering techniques. First, by exploitinga novel topic model namely UI-LDA, we obtain the representationvectorsfor news content in topic spaceas the fundamentalbridgeto associate user interests with news topics. Next, we design a decision tree with a dynamically changeable structure to construct a user interest profile from his feedback. Furthermore, we present a clustering based multidimensional similarity computation method to select the nearest neighbor of UP-Treeefficiently. We also provide a Map-Reduce framework based implementation that enables scaling our solution to real-world news recommendation problems.We conducted several experiments compared to the state-of-the-art approaches on real-world datasets and the experimental results demonstrate that our approach significantly improves the accuracy and effectiveness in news recommendation.
162Scopus© Citations 10
- PublicationOverlap Training to Mitigate Inconsistencies Caused by Image Tiling in CNNsThis paper focuses on the problem of inconsistent predictions of modern convolutional neural networks (CNN) at patch (i.e. sub-image) boundaries. Limited by the graphics processing unit (GPU) resources, image tiling and stitching countermeasure have been applied for most megapixel images, that is, cutting images into overlapping tiles as CNN input, and then stitching CNN outputs together. However, we found that stitched (i.e. recovered) predictions have discontinuous grid-like noise. We propose a simple yet efficient overlap training framework to mitigate the inconsistent prediction at patch boundaries without changing the model architecture while improving the stability, robustness of the model. We have applied our solution to various CNNs (such as U-Net, DeepLab, RCF) and tested them on two real-world datasets. Extensive experiments suggest that the new framework is sufficient in reducing inconsistency and outperform these countermeasures. The source code and coloured figures are made publicly available online at: https://github.com/anyuzoey/Overlap-Training.git.
Scopus© Citations 3 371
- PublicationOpinionated Product RecommendationIn this paper we describe a novel approach to case-based product recommendation. It is novel because it does not leverage the usual static, feature-based, purely similarity-driven approaches of traditional case-based recommenders. Instead we harness experiential cases, which are automatically mined from user generated reviews, and we use these as the basis for a form of recommendation that emphasises similarity and sentiment. We test our approach in a realistic product recommendation setting by using live-product data and user reviews.
Scopus© Citations 42 711
- PublicationFrom More-Like-This to Better-Than-This: Hotel Recommendations from User Generated ReviewsTo help users discover relevant products and items recommender systems must learn about the likes and dislikes of users and the pros and cons of items. In this paper, we present a novel approach to building rich feature-based user profiles and item descriptions by mining user-generated reviews. We show how this information can be integrated into recommender systems to deliver better recommendations and an improved user experience.
Scopus© Citations 9 338
- PublicationExplainable Text-Driven Neural Network for Stock PredictionIt has been shown that financial news leads to the fluctuation of stock prices. However, previous work on news-driven financial market prediction focused only on predicting stock price movement without providing an explanation. In this paper, we propose a dual-layer attention-based neural network to address this issue. In the initial stage, we introduce a knowledge-based method to adaptively extract relevant financial news. Then, we use an input attention to pay more attention to the more influential news and concatenate the day embeddings with the output of the news representation. Finally, we use an output attention mechanism to allocate different weights to different days in terms of their contribution to stock price movement. Thorough empirical studies based upon historical prices of several individual stocks demonstrate the superiority of our proposed method in stock price prediction compared to state-of-the-art methods.
705Scopus© Citations 27
- PublicationSentimental Product RecommendationThis paper describes a novel approach to product recommendation that is based on opinionated product descriptions that are automatically mined from user-generated product reviews. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers. We demonstrate the benefits of this approach across a variety of Amazon product domains.
Scopus© Citations 51 626
- PublicationPersonalised Opinion-based RecommendationE-commerce recommender systems seek out matches betweencustomers and items in order to help customers discover more relevantand satisfying products and to increase the conversion rate of browsers tobuyers. To do this, a recommender system must learn about the likes anddislikes of customers/users as well as the advantages and disadvantages(pros and cons) of products. Recently, the explosion of user-generatedcontent, especially customer reviews, and other forms of opinionated expression,has provided a new source of user and product insights. Theinterests of a user can be mined from the reviews that they write andthe pros and cons of products can be mined from the reviews writtenabout them. In this paper, we build on recent work in this area to generateuser and product proles from user-generated reviews. We furtherdescribe how this information can be used in various recommendationtasks to suggest high-quality and relevant items to users based on eitheran explicit query or their prole. We evaluate these ideas using alarge dataset of TripAdvisor reviews. The results show the benets ofcombining sentiment and similarity in both query-based and user-basedrecommendation scenarios, and also disclose the eect of the number ofreviews written by a user on recommendation performance.
Scopus© Citations 6 408
- PublicationHTML: Hierarchical Transformer-based Multi-task Learning for Volatility PredictionThevolatility forecastingtask refers to predicting the amount ofvariability in the price of a financial asset over a certain period.It is an important mechanism for evaluating the risk associatedwith an asset and, as such, is of significant theoretical and practicalimportance in financial analysis. While classical approaches haveframed this task as a time-series prediction one – using historicalpricing as a guide to future risk forecasting – recent advances innatural language processing have seen researchers turn to com-plementary sources of data, such as analyst reports, social media,and even the audio data from earnings calls. This paper proposes anovel hierarchical, transformer, multi-task architecture designedto harness the text and audio data from quarterly earnings confer-ence calls to predict future price volatility in the short and longterm. This includes a comprehensive comparison to a variety ofbaselines, which demonstrates very significant improvements inprediction accuracy, in the range 17% - 49% compared to the currentstate-of-the-art. In addition, we describe the results of an ablationstudy to evaluate the relative contributions of each component ofour approach and the relative contributions of text and audio datawith respect to prediction accuracy.
Scopus© Citations 62 266
- PublicationUser-based Opinion-based RecommendationUser-generated reviews are a plentiful source of user opinions and interests and can play an important role in a range of artificial intelligence contexts, particularly when it comes to recommender systems. In this paper, we describe how natural language processing and opinion mining techniques can be used to automatically mine useful recommendation knowledge from user generated reviews and how this information can be used by recommender systems in a number of classical settings.
Scopus© Citations 11 390