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With the incredibly growing amount of multimedia data uploaded and shared via the social media web sites, recommender systems have become an important necessity to ease users'burden on the information overload. In such a scenario, extensive amount of content information, such as tags, image content and user to item preferences are also available and extremely valuable for making effective recommendations...
This paper presents a personalized recommendation system mining online product reviews, fusing opinions together and providing a ranked order of a set similar products. We define three attributes of opinion summary: opinion coverage, opinion consistency and opinion consensus. Confidence factor is computed based on these attributes. A user specifies the relative importance of each product feature....
We consider the problem of the filtering of Twitter posts, that is, the hiding of those posts which the user prefers not to visualize on his/her timeline. We define a language for specifying filtering policies suitable for Twitter posts. The language allows each user to decide which posts to filter out based on his/her sensibility and preferences. Since average users may not have the skills necessary...
In this paper, a simulation of a multi-agent recommender system is presented and developed in the NetLogo platform. The specification of this recommender system is based on the well known Belief-Desire-Intention agent architecture applied to multi-context systems, extended with contexts for additional reasoning abilities, especially social ones. The main goal of this simulation study is, besides illustrating...
In many shopping sites such as Amazon.com it is possible to view and write reviews of items (products and content). Reviews of items including stories, such as novels, movies, and comics, include reviewers' opinions. Often, these reviews also include descriptions of the story. In some cases, these descriptions may spoil later reader's or viewer's enjoyment and excitement. Hereinafter, we call these...
A combination of multiple retrieval systems can outperform its individual component systems, but it remains a challenging problem to predict whether two systems can be beneficially combined and, if so, the optimal means by which they should be merged. The performance of combined systems is affected by many factors, including the performance of individual systems, the diversity between a pair of systems,...
A picture lifelog is a type of lifelog that consists of pictures, mainly taken by the user. Recently, users have been able to easily create picture lifelogs because many portable devices such as smart phones have a camera. When a user sees a picture in their picture lifelog, it is sometimes difficult to recall the events related to the picture. Therefore, we proposed to combine search queries on a...
3D Surfaces are widely employed to model geometric assets (e.g., mountains on a landscape), which are used in digital animations and video games. A single surface commonly needs to be created and modified by a group of collaborators, but most of the 3D content creation applications are essentially single-user. In addition, such surfaces are visualized in 2D projections, causing confusion to new users,...
Social media provides increasing opportunities for users to voluntarily share their thoughts and concerns in a large volume of data. While user-generated data from each individual may not provide considerable information, when combined, they include hidden variables, which may convey significant events. In this paper, we pursue the question of whether social media context can provide socio-behavior...
FAQs are the lists of common questions and answers on particular topics. Today one can find them in almost all web sites on the internet and they can be a great tool to give information to the users. Questions in FAQs are usually identified by the site administrators on the basis of the questions that are asked by their users. While such questions can respond to required information about a service,...
The automatic detection of emotions in Twitter posts is a challenging task due to the informal nature of the language used in this platform. In this paper, we propose a methodology for expanding the NRC word-emotion association lexicon for the language used in Twitter. We perform this expansion using multi-label classification of words and compare different word-level features extracted from unlabelled...
Hierarchical clustering has been well-studied in the community of machine learning. Hierarchical clustering algorithms are deterministic, stable, and do not need a pre-determined number of clusters as input. However, they are not scalable for very large data due to their non-linear complexity. In this paper, a new approach is proposed to reduce the complexity of Hierarchical Clustering, improve the...
Previous studies have used many manually identified features and word embeddings for tweet sentiment classification. In this paper, we propose a new approach, which incorporates sentiment-specific word embeddings (SSWE) and a weighted text feature model (WTFM). WTFM produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. Compared to other tweet...
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