My Portal Viewer: Integration System Based on User Preferences for News Web Sites

2005 ◽  
pp. 156-165 ◽  
Author(s):  
Yukiko Kawai ◽  
Daisuke Kanjo ◽  
Katsumi Tanaka
Keyword(s):  
Web Sites ◽  
User Preferences ◽  
Integration System ◽  
News Web Sites

Collaborative Filtering for Information Recommendation Systems

2011 ◽  
pp. 118-123 ◽  
Author(s):  
Anne Yun-An Chen ◽  
Dennis McLeod
Keyword(s):  
Collaborative Filtering ◽  
Web Sites ◽  
Information Search ◽  
Recommendation Systems ◽  
User Preferences ◽  
Data Sets ◽  
Online Information ◽  
Filtering Algorithms ◽  
User Data ◽  
Filtering Techniques

In order to draw users’ attention and to increase their satisfaction toward online information search results, search-engine developers and vendors try to predict user preferences based on users’ behavior. Recommendations are provided by the search engines or online vendors to the users. Recommendation systems are implemented on commercial and nonprofit Web sites to predict user preferences. For commercial Web sites, accurate predictions may result in higher selling rates. The main functions of recommendation systems include analyzing user data and extracting useful information for further predictions. Recommendation systems are designed to allow users to locate preferable items quickly and to avoid possible information overload. Recommendation systems apply data-mining techniques to determine the similarity among thousands or even millions of data. Collaborative-filtering techniques have been successful in enabling the prediction of user preferences in recommendation systems (Hill, Stead, Rosenstein, & Furnas, 1995, Shardanand & Maes, 1995). There are three major processes in recommendation systems: object data collections and representations, similarity decisions, and recommendation computations. Collaborative filtering aims at finding the relationships among new individual data and existing data in order to further determine their similarity and provide recommendations. How to define the similarity is an important issue. How similar should two objects be in order to finalize the preference prediction? Similarity decisions are concluded differently by collaborative-filtering techniques. For example, people that like and dislike movies in the same categories would be considered as the ones with similar behavior (Chee, Han, & Wang, 2001). The concept of the nearest-neighbor algorithm has been included in the implementation of recommendation systems (Resnick, Iacovou, Suchak, Bergstrom, & Riedl, 1994). The designs of pioneer recommendation systems focus on entertainment fields (Dahlen, Konstan, Herlocker, Good, Borchers, & Riedl, 1998; Resnick et al.; Shardanand & Maes; Hill et al.). The challenge of conventional collaborative-filtering algorithms is the scalability issue (Sarwar, Karypis, Konstan, & Riedl, 2000a). Conventional algorithms explore the relationships among system users in large data sets. User data are dynamic, which means the data vary within a short time period. Current users may change their behavior patterns, and new users may enter the system at any moment. Millions of user data, which are called neighbors, are to be examined in real time in order to provide recommendations (Herlocker, Konstan, Borchers, & Riedl, 1999). Searching among millions of neighbors is a time-consuming process. To solve this, item-based collaborative-filtering algorithms are proposed to enable reductions of computations because properties of items are relatively static (Sarwar, Karypis, Konstan, & Riedl, 2001). Suggest is a top-N recommendation engine implemented with item-based recommendation algorithms (Deshpande & Karypis, 2004; Karypis, 2000). Meanwhile, the amount of items is usually less than the number of users. In early 2004, Amazon Investor Relations (2004) stated that the Amazon.com apparel and accessories store provided about 150,000 items but had more than 1 million customer accounts that had ordered from this store. Amazon.com employs an item-based algorithm for collaborative-filtering-based recommendations (Linden, Smith, & York, 2003) to avoid the disadvantages of conventional collaborative-filtering algorithms.

Trends emerging more clearly: Business models of news Web Sites

First Monday ◽  
2006 ◽  
Author(s):  
Fred Schiff
Keyword(s):  
Web Sites ◽  
Business Models ◽  
The Internet ◽  
Special Issue ◽  
News Web Sites ◽  
Commercial Applications

This paper is included in the First Monday Special Issue #6: Commercial applications of the Internet, published in July 2006. Special Issue editor Mark A. Fox asked authors to submit additional comments regarding their articles.

Business News Web Sites

2005 ◽  
Vol 10 (4) ◽  
pp. 41-41
Author(s):  
Gary W. White
Keyword(s):  
Web Sites ◽  
Business News ◽  
News Web Sites

scholarly journals From Consumer Demand to User Engagement: Comparing the Popularity and Virality of Election Coverage on the Internet

2018 ◽  
Vol 24 (1) ◽  
pp. 49-68
Author(s):  
Jacob Ørmen
Keyword(s):  
Social Network ◽  
Web Sites ◽  
Consumer Demand ◽  
Social Network Sites ◽  
News Coverage ◽  
Parliamentary Election ◽  
User Engagement ◽  
News Stories ◽  
Election Coverage ◽  
News Web Sites

Previous research has identified a strong consumer demand for sensationalized and conflict-oriented news coverage. With the rise of social network services as central spaces for encountering news, there is a need to move beyond the notion of consumer demand (measured by attention to news stories) to a broader conception of user engagement (encompassing attention as well as social interactions online). This article seeks to remedy this by analyzing which parts of election coverage tend to become popular and go viral. It develops a concept of user agendas that include popularity (news stories that receive most clicks on news Web sites) and virality (stories that users share most intensively on social network sites). The article then applies the concepts in a case study of online news coverage during the 2015 Danish parliamentary election. Through an analysis of frames, sentiments, and actors, it is shown that game-strategic and personalized coverage tend to attract large-scale attention on news Web sites, whereas issue-oriented coverage fares better on social network sites. This suggests that what users demand depend on where they encounter news. Users tend to engage with one kind of news in private settings and another in the public settings on the social Internet.

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