Download Advances in Recommender Systems (Smart Innovation, Systems by Lakhmi C. Jain, George A. Tsihrintzis, Maria Virvou PDF

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

Multimedia providers at the moment are regularly occurring in a number of actions within the day-by-day lives of people. similar software parts contain companies that permit entry to giant depositories of knowledge, 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 large progress of multimedia prone over the hot years, there's an expanding call for for his or her extra improvement. This call for is pushed through the ever-increasing hope of society for simple accessibility to info in pleasant, customized and adaptive environments.

In this publication to hand, we learn contemporary Advances in Recommender platforms. Recommender platforms are an important in multimedia providers, as they target at maintaining the carrier clients from information overload. The e-book contains 9 chapters, which current numerous contemporary learn leads to recommender systems.

This study ebook is directed to professors, researchers, program engineers and scholars of all disciplines who're attracted to studying extra approximately recommender structures, advancing the corresponding state-of-the-art 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)

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Our solution is to improve the effectiveness of a user in a search by developing a hybrid user model to capture user intent dynamically and combines the captured intent with an awareness of the components of an information retrieval system. The term ‘‘hybrid’’ refers to the methodology of combining the understanding of a user with the insights into a system all unified within a decision theoretic framework. In this model, multi-attribute utility theory is used to evaluate values of the attributes describing a user’s intent in combination with the attributes describing an information retrieval system.

Note that in this algorithm, we treat the nodes with one parent differently from those with multiple parents. The intuition is that a single parent only will have stronger influence on its children while the influence from multiple parents needs to be aggregated to avoid bias from a specific parent. A Context network is dynamically constructed by finding the intersection of all document graphs representing retrieved relevant documents. The algorithm to find the intersection of the retrieved relevant document is shown earlier in the previous subsection.

We comprehensively evaluate our hybrid user model and compare it with the best traditional approach for relevance feedback in the IR community— Ide dec-hi using term frequency inverted document frequency weighting on selected collections from the IR community such as CRANFIELD, MEDLINE, and CACM. The results show that with the hybrid user model, we retrieve more relevant documents in the initial run compared to the Ide dec-hi approach. Our hybrid user model also performs better with the MEDLINE collection compared to our user modeling approach using only a user’s intent [58].

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