Using Content Based Filtering Technique

Privacy in this era is threatened by the growth in technology with capacity that is enhanced for surveillance, storage, communication as well as computation. Moreover, the increased value of this information in decision making is one of the insidious threats. For this reason, information and its privacy are actually threatened and less privacy is assured.

Programs are easier to write than papers for some of us students, myself included. I feel as though with the future comes change - that change is that we, the students, should have the option to develop an in depth program (solely) instead of an antiquated, tautological, and outmoded paper for our classes. Some of us have more advanced programming skills and would like a chance to use them instead of our weaker research and documentation skills. There is no enjoyment on researching topics like this for me, I loathed every second of research and production of this paper. Research on how code works and what to do with that code, the algorithms and routines that you can do with it is very stimulating for me. Please, feel my effusive abhor while you read.

This process is two fold. First, the model learns the different aspects that are distributed over a set of decided words and also learn their scores. Second, the model forecasts the aspects of the user for an item for a future review. For the first step, a vector of user preferences of aspects of an item, a vector of aspects of an item are used. The Dot product of the two vectors are used for the prediction step.

An overall review score is already assigned by the user when he/she writes the reviews. From these, the proposed probabilistic model calculates the aspect score for each aspect from the user's list of preferences and presents those reviews that has the best aspect score from different reviews. The model implicitly predicts the user aspects and their scores from the review provided by the user and then mines for the reviews that match the aspect score similar to those of the

Privacy, a term that is reluctant in today’s modernized society who’s meaning is far from realistic. As the world congruently fosters a new way of living, through technology our lives as we know as our own is far from it. This paper will discuss the use of technology to research ones private information, the advantages and disadvantages of the public access of such information and the laws that promulgate such data.

Over the past few years, the development of the Internet and the intrusive surveillance capabilities of these technologies have caused privacy to become a major political and social issue for millions of Americans who go online.  Companies employ a variety of tools to gather marketable information on American citizens.  Most of the use of this information is for personalized advertisement and to create databases of target audiences.  While these activities may appear to be nothing more than annoyances for a majority of Americans, there is the hidden danger of the loss of privacy.

When you have to research something at school for class, you get it. The CIPA filter logo. It reads access denied. Every time you get it you ask the same question, why? Signed into law on December 21, 2001, the Children's Internet Protection Act changed the way schools internet access works. This mostly was because of a court case called Miller vs California. The case involved Mr. Marvin Miller and a restaurant. Miller was charged with violating California Penal Code 311.2, a very serious crime. Marvin was the owner of a business in which they made pornoographic content. Marvin wanted to promote his business so he sent out brochures of his company. Many companies and people received these brochures but one restaurant received 5 brochures from

Congress as a bill in January, 2003 (see H.R. 69).  Even though OPPA is just proposed legislation at this point, it encompasses most of the necessary components for comprehensive protection of privacy online called for by privacy advocates and entities such as the FTC. It is also consistent with the Fair Information Practices (FIP) [9], which have operated as a guide for policy makers in the U.S. If the U.S. does indeed enact comprehensive online privacy legislation, it will most likely continue to use the FIPs as a guide and therefore, will closely resemble OPPA.   The results presented herein will benefit managers and website designers of companies involved in international business, as well as policy makers.

To find information on the controversial topic, a survey was mailed in April 2000 to a randomly selected sample of participants from schools and public libraries that had used web filters in the past. The purpose of the survey was to find out how effective web filters are at doing their job(Curry). The results of the survey provided staggering evidence against the use web filters. Almost 50% (43%) of the respondents voted that they felt somewhat dissatisfied or not at all satisfied with the ability of the web filters to block potentially harmful sites. This shows that large holes inhabitat the web filters and their ability to remove all the possible negativity from the internet happens to remain very

Anyone who knows me knows that I care an awful lot about politics and like to stay up to date with current events. One day when I free time in study hall I chose to take advantage of the the opportunity I had to check the news on my favorite news site INFO WARS. When I did so, a sign appeared on my computer screen that read “This site has been blocked because it contains illegitimate political content”. Frustrated, I tried to go over to my second favorite news site DRUDGE REPORT, which was thankfully not blocked. However, when I clicked on an interesting article titled “Dozens killed in Belgium by suicide bomber” the same sign appeared yet again that read “This site has been blocked. Keyword: bomb”. It was extremely annoying how these sites that I had been to before and knew were not inappropriate could not be accessed. The internet filters that the school claimed was

This paper describes and informs the reader of material about Information Privacy. It gives a background as to what Information Privacy is, and educates the reader on more about the subject. This paper includes definitions, past and present examples and details about the subject, and Laws relating to the subject.

Personal privacy today is a controversial and complex topic, which is influenced by a number of factors. There is an integral role that databases play in this highly debated topic. The fact that many people now carry out their transactions electronically is another important factor. There is also pressure on personal privacy for increased national security around the world to combat terrorism. In addition, personal privacy is even threatened by commercial factors and the Internet.

In the era of information rich technology, society has unwittingly exposed individual identities to big corporations online, whereby intensely secure information of oneself is used to stimulate revenue. With digital profiles that are created and used daily on social networking mediums such as Facebook, individuals are creating a platform for data mining, phishing and also malicious activities of online predators. There are many large corporations that claim to keep information confidential, which strengthens trust with users to distribute personal information within their databases. This can be elucidated with the use of cloud based services, and it is concerning the activities are executed with this access of information. Some of these questionable factors about confidentiality are mentioned in the terms and conditions of a site, that are read by the scarce minority of people. Nonetheless, with instances such as the cyber-attack of Sony’s PlayStation Network, it is evident that there is little to no information that is secure in the digital world. It is therefore evident that possessing knowledge about these issues will lead to greater security.

till this stage we given only one valid usename and pass- word.that is admin@gmail.com and admin respectively. Who knows this data can use our ranking system.shown in Figure 10. After logging in, user can have access to profile, Upload document, show products, change password, as shown in Figure 11, Figure 12, Figure 13, Figure 14. 1st user has to find and download html pages of the prod- ucts of which ranking is to be calculated from Amazon.com.

The system crawls all the information (customer reviews) from the particular product and starts to extract the feature words. The mining classification is divided into three subtasks. First, POS tagging, most product feature in reviews contain nouns and phrases. POS enables to identify these tags words by word. The authors have used NLProcessor linguistic parser to split the sentences and produce POS tag for every individual word. Some pre-processing is performed to remove stop words to avoid big POS tag lists. Fuzzy matching was also used to deal with misspelling and word variants. Second, identifying frequent features, features that are particularly used by customers in many reviews. To put in context, words, or sentences that occur together in many reviews. The authors use association mining to list out these words. An apriori algorithm based association miner is used to identify these features/words. The gathered features cannot possibly be genuine product features. Pruning enables to remove the unwanted features/words that don’t match to product opinion or feature. The author uses two pruning methods and compares their efficiency. While using compactness pruning, it looks for features that is composed of two or more words and identifies if they make