Neighbourhood-based collaborative filtering is one of the most popular recommendation techniques, and has been applied successfully in various fields. User ratings are often used by neighbourhood-based collaborative filtering to compute the similarity between two users or items, but, user ratings may not always be representatives of their true preferences, resulting in unreliable similarity inf...

This paper proposes a new HDP based online review rating regression model named TopicSentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as ”critical aspect” analysis altogether in one framework....

The rapid growth of communication technologies and the invention of set-top-box (STB) and personal digital recorder (PDR) have enabled today’s television to receive and store tremendous programs. The abundance of TV programs precipitates a need for personalization tools to help people obtain programs that they really want to watch. User preference learning plays an important role in TV program ...

Podcasts display an unevenness characteristic of domains dominated by user generated content, resulting in potentially radical variation of the user preference they enjoy. We report on work that uses easily extractable surface features of podcasts in order to achieve solid performance on two podcast preference prediction tasks: classification of preferred vs. non-preferred podcasts and ranking ...

In order to predict user-favorite songs, managing user preferences information and genre classification are necessary. In this paper, we propose a preference classification about content based on real-time user brainwave and a music recommendation system based on it. We focused on classifying real-time user preferences by analyzing the user’s brainwaves. The brainwaves are acquired using a wire...

Most current network resources recommendation systems are used in a decision-making environment with low user participation, which can not effectively meet user expectations. The main reason is that the collection of user preferences is extremely difficult, which leads to the lack of information acquisition, and the lack of effective semantic similarity metric. Accordingly, this paper uses onto...

aBsTracT Human Computer Interaction (HCI) challenges in highly dynamic computing environments can be solved by tailoring the access and use of services to user preferences. In this era of emerging standards for open and collaborative computing environments, the major challenge that is being addressed in this chapter is how personalisation information can be managed in order to support cross-ser...

†‡5180 Kurokawa Imizu, Toyama Prefectural University, Toyama, Japan E-mail: †828kazuya@[email protected], ‡{iwamoto, michito}@ pu-toyama.ac.jp Abstract Today, there are various planning systems that create outing plans for trips and drives, and support for tourists. The existing systems are designed to use user preference information to plan suitable trip plans for the user. However,...

Preferences have gained a tremendous impact on the personalization of user queries. In this paper, we present an approach for preference elicitation in a multimedia information retrieval scenario. The approach is based on inductive preferences, which provide an intuitive means for stating preferences on actual result documents. Additionally, they do not demand further knowledge of the underlyin...