By Lakhmi C. Jain, George A. Tsihrintzis, Maria Virvou

ISBN-10: 3319003712

ISBN-13: 9783319003719

Multimedia companies are actually universal in a variety of actions within the day-by-day lives of people. similar software components contain providers that let entry to giant depositories of data, electronic libraries, e-learning and e-education, e-government and e-governance, e-commerce and e-auctions, e-entertainment, e-health and e-medicine, and e-legal providers, in addition to their cellular opposite numbers (i.e., m-services). regardless of the super progress of multimedia prone over the hot years, there's an expanding call for for his or her additional improvement. This call for is pushed through the ever-increasing wish of society for simple accessibility to details in pleasant, customized and adaptive environments.

In this ebook handy, we research contemporary Advances in Recommender platforms. Recommender platforms are an important in multimedia providers, as they target at conserving the carrier clients from information overload. The e-book contains 9 chapters, which current a variety of fresh examine ends up in recommender systems.

This examine booklet is directed to professors, researchers, software engineers and scholars of all disciplines who're attracted to studying extra approximately recommender platforms, advancing the corresponding cutting-edge and constructing recommender structures for particular applications.

 

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Additional resources for Advances in Recommender Systems (Smart Innovation, Systems and Technologies: Multimedia Services in Intelligent Environments Volume 24)

Sample text

A user shows his/her preferences in the ways to modify a query by choosing from a class of tools. We currently choose two tools to implement in our Preference network which are filters and expanders. A filter is a tool that searches for documents that narrows the search topics semantically and an expander that searches for documents that broaden the search topics semantically. We chose these two tools because they are Hybrid User Model for Capturing a User’s Information Seeking Intent Vector SpreadingActivation(I, Q, C) { /*Initializing bias is executed as follows: (i) set the bias equal to 1 for every concept found both in the current context network and in the current query graph; and (ii) set bias equal to the interest level for every interest concept found both in the current context network and in the current interest set.

In our approach, we use the information of the components of an IR system to predict the effectiveness of a user in a current search. From the IR community, researchers in probabilistic IR have used decision theory to determine indexing schemes [20], however, no attention has been paid to users. 1 Overview In this work, we develop a hybrid user model to improve effectiveness of an IR system. User models are needed on top of an IR system because the traditional IR framework does not utilize much input from a user except a user’s query and some relevance feedback.

Because the recommendations are based on prior knowledge, there is no learning time before making good recommendations. However, the main drawback of knowledge-based systems is a need for knowledge acquisition for the specific domain which makes difficult the adaptation in another domain and not easily adapted to the individual user as it is enhanced by predefined recommendations. 4 Fundamental Problems of Recommender Systems Cold Start Problem. The cold-start problem [54] is related to the learning rate curve of a RS.

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Advances in Recommender Systems (Smart Innovation, Systems and Technologies: Multimedia Services in Intelligent Environments Volume 24) by Lakhmi C. Jain, George A. Tsihrintzis, Maria Virvou


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