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Existing rating and reviewing schemes typically come in the in the flavor of a single rating and/or a textual review. While a single judgment evaluating the overall quality of a product is of limited significance, textual customer reviews typically deliver more informative feedback at the attribute level. However, reading and comparing them to extract relevant information is time-consuming and mentally-demanding...
Social media is becoming a major and popular technological platform that allows users to express personal opinions toward the subjects with shared interests. Identifying the sentiments of these social media data can help users make informed decisions. Existing research mainly focus on developing algorithms by mining textual information in social media. However, none of them collectively consider the...
In this paper, we propose an item recommendation algorithm based on latent factors which uses implicit feedback from users to optimize the ranking of items according to individual preferences. The novelty of the algorithm is the integration of content metadata to improve the quality of recommendations. Such descriptions are an important source to construct a personalized set of items which are meaningfully...
Recommender systems profile the preferences of users and then use this information to forecast users' future ratings. One of the most common recommendation approaches is the use of matrix factorisation in which users' past ratings of items (i.e. Movies, books, etc.) are used to capture their affinity to implicit factors. A central limitation of such factorisation is that one cannot consider how a...
Structured knowledge bases are an increasingly important way for storing and retrieving information. Within such knowledge bases, an important search task is finding similar entities based on one or more example entities. We present QBEES, a novel framework for defining entity similarity based only on structural features, so-called aspects, of the entities, that naturally model potential interest...
Reputation analysis is naturally associated to a sentiment analysis task of the targeted named-entities. This analysis leverages on a sentiment lexicon that includes general sentiment words that characterize the general sentiment towards the targeted named-entity. However, in most cases, target entities are themselves part of the sentiment lexicon, creating a loop from which it is difficult to infer...
Standard Collaborative Filtering (CF) algorithms make use of interactions between users and items in the form of implicit or explicit ratings alone for generating recommendations. Similarity among users or items is calculated purely based on rating overlap in this case, without considering explicit properties of users or items involved, limiting their applicability in domains with very sparse rating...
Posting online reviews and rating their satisfaction on purchased products has become an increasingly popular way to share the information for anonymous candidates who has interest in purchasing the product. In addition, people leave their interests and near-future purchasing plan on the web such as search history and search query volume. From this phenomenon, the prediction of sales performance is...
Context-aware recommender systems (CARS) are extensions of traditional recommenders that also take into account contextual condition of a user to whom a recommendation is made. The recommendation problem is, however, still focused on recommending a set of items to a target user. In this paper, we consider the problem of recommending to a user the appropriate contexts in which an item should be selected...
Due to rapid development of agent systems and robotics, more and more chances are available for humans to interact with agent-based robotic technology (e.g., Robotic vacuums, robotic surgery, etc.), this trend increases the importance of human-robot interaction including human-robot communication. For the robust human-robot communication, natural language processing (NLP) can be implemented, among...
Many shopping sites provide functions to submit a user review for a purchased item. Reviews of items including stories such as novels and movies sometimes contain spoilers (undesired and revealing plot descriptions) along with the opinions of the review author. In this paper, we propose a system that helps users see reviews without seeing such plot descriptions. This system classifies each sentence...
The internet and the Web 2.0 gave rise to a wide variety of user generated content. This caused a massive growth in the amount and availability of opinionated information. This collection of complex, unstructured information is often referred as Big Data. A common practical application of such Big Data is social media sentiment analysis. The general aim of sentiment analysis is to determine/extract...
Sentiment analysis has now become a popular research problem to tackle in NLP field. However, there are very few researches conducted on sentiment analysis for Chinese. Progress is held back due to lack of large and labelled corpus and powerful models. To remedy this deficiency, we build a Chinese Sentiment Treebank over social data. It concludes 13550 labeled sentences which are from movie reviews...
This paper proposes a conceptual framework which uses multimodal user feedback to generate a more accurate personalized ranking of items to the user. Our technique is a response to the actual scenario on the Web, where users can consume content following different interaction paradigms, such as rating, browsing, sharing, etc. We developed a post-processing step to ensemble rankings generated by unimodal-based...
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