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Deep neural networks have yielded immense success in speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks for content based recommendation has received a relatively less amount of inspection. Also, different recommendation scenarios have their own issues which creates the need for different approaches for recommendation. One of the problems...
while e-commerce has grown quickly in recent years, more and more people are used to utilize this popular channel to purchase products and services on the Internet. Therefore, it becomes very important for shopping sites to predict precisely which items their customers would buy so as to increase sales or improve customer satisfaction. Traditional algorithms such as Collaborative Filtering, has been...
With the development of the Internet, e-commerce industry rises rapidly. Online shopping becomes more and more convenient and fast. However it is very difficult for consumers to find satisfied commodity because of abundant and mixed commodity. Especially when people purchase the items which they are not familiar with or consume in a strange place. The study of the recommended system is to figure out...
Microblogging services have been popular in recent years. There are a large number of real-time microblog messages generated in each day which results in the information overload problem especially for the users with many followees. Personalized microblog recommendation can help the users out of the trouble of information overload. It is an interesting and important research topic with wide applications...
GitHub is one of the most commonly used web-based code repository hosting service. Majority of projects hosted on GitHub are really small but, on the other hand, developers spend most of their time working in medium to large repositories. Developers can freely join and leave projects following their current needs and interests. Based on real data collected from GitHub we have tried to predict which...
Twitter users get the latest tweets of their followees on their timeline. In this work we present a tweet recommendation approach, which takes advantage of the semantic relatedness of concepts that interest users. Our approach could be leveraged to build an efficient, online tweet recommender. We construct a Concept Graph (CG), containing a variety of concepts, use graph theory algorithms not yet...
This paper describes a method for extracting the search contexts on the basis of the analysis of search history data such as viewed Web pages, search queries, and bookmarks during collaborative exploration activities. There are many opportunities for collaborative exploration in the form of cooperative work at educational organizations such as universities. It is quite significant for collaborative...
Our music recommendation system recommends a song to a user, at a certain time, based on the listening history of the user. Based on different sets of audio features (MFCC, MPITCH, BEAT, STFT) of all available songs, different clusterings of songs are obtained. Users are given recommendations from one of these clusterings. The right clustering for a user is determined based on the Shannon entropy...
In IT strategic outsourcing businesses, it is critical to have competent deal teams design competitive service solutions and swiftly respond to clients' requests for proposals. In this paper we present a general team recommendation framework for finding the best deal teams to pursue such engagement opportunities. Little previous work on team recommendations considers both individual and team-level...
Nowadays recommendation systems are widely used in E-Commerce. They can learn about user interests and automatically suggest the best product to the consumer. Most of these recommendation systems are using collaborative, content-based or knowledge-based method. Users and products can gather in some groups based on their similar features. Using these groups can improve their recommendations and help...
Sequential fuzzy co-cluster extraction has been proven to be useful for collaborative filtering tasks by extracting user-item co-clusters, in which promising items are connected to the corresponding users in each co-cluster. Since some popular items can be shared by multiple clusters in collaborative filtering problems, exclusive conditions, which force objects to belong to only one cluster, were...
In order to make recommendations to a user, a recommender mainly uses two approaches: content-based-filtering approach and collaborative filtering approach. However, they both still have some shortcomings technically. The content-based approach is difficult to handle feature extraction as well as user intension prediction. The collaborative approach faces the hard issue of cold start problem and the...
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