With the rapid development of network technology and entertainment creation, the types of
movies have become more and more diverse, which makes users wonder how to choose …

Abstract Information retrieval systems strive for catering user's information need in terms of
providing most relevant documents with regards to user's query. Despite of decades of …

The inclusion of collective intelligence through a semantic focused affective computing can
incorporate intelligence to web search and ensure its compliance with the Web 3.0. In this …

Collaborative and content-based filtering are the majorly used approaches towards
implementing recommender systems which can successfully predict the item (s) to be …

Music is an integral part of our everyday life. Almost everyone uses various streaming
services to listen to music on their mobile phones. These streaming services recommend …

Search engines often recommend a few queries related to the users' original query to help
them find the content they are searching. Since extracting the user's intent from the query is …

A novel model of data similarity estimation and clustering method is proposed in this article
to retrieve the relevant data with the best matching in big data processing. An advanced …

Collaborative filtering (CF) and content-based filtering (CBF) are the two primary methods for
building Recommender Systems (RS), aiming to predict items that users may like based on …

Recommendation systems are a very popular service whose accuracy and sophistication
keeps increasing every day. Yet current systems pose a limitation on personalized user …

Community detection is a dominant research field in social media mining. Numerous
algorithms have been developed to detect community structure in network. Each of them …