Content-Based Filtering, Ontology

Researchers initially have addressed the problem of spam detection as a text classification or categorization problem. However, as spammers’ continue to develop new techniques and the type of email content becomes more disparate, text-based anti-spam approaches alone are not sufficiently enough in preventing spam. In an attempt to defeat the anti-spam development technologies, spammers have recently adopted the image spam trick to make the scrutiny of emails’ body text inefficient. The main idea behind this project is to design a spam detection system. The system will be enabled to analyze the content of emails, in particular the artificially generated image sent as attachment in an email. The system will analyze the image content and classify the embedded image as spam or legitimate hence classify the email accordingly.

The gigantic growth of information on the Internet makes discovery information challenging and time consuming. We are encircled by a plethora of data in the form of blogs, papers, reviews, and comments on different websites. Recommender systems endow a solution to this situation by automatically capturing user interests and recommending respective information the user may also find relevant. The purpose of developing recommender systems is to detract information overload by retrieving the most pertinent knowledge and services from an enormous amount of data, thereby providing personalized services. The most vital feature of a recommender system is its proficiency to "supposition" a user's preferences and interests by examining the behavior of this user and/or the behavior of other users to originate personalized recommendations. So several research works have been done in this area, but nothing consolidated has been appraised. In this paper, we are going to discuss a brief summary of imperfection in the available recommender system. We are also trying to figure out these shortcomings of the available recommender system to generate a new method that improves these shortcomings.

Purpose – The purpose of this paper is to describe a new ontological content-based filtering method for ranking the relevance of items for readers of news items, and its evaluation. The method has been implemented in ePaper, a personalised electronic newspaper prototype system. The method utilises a hierarchical ontology of news; it considers common and related concepts appearing in a user’s profile on the one hand, and in a news item’s profile on the other hand, and measures the “hierarchical distances” between these concepts. On that basis it computes the similarity between item and user profiles and rank-orders the news items according to their relevance to each user.
Design/methodology/approach – The paper evaluates the performance of the filtering method in an experimental setting. Each participant read news items obtained from an electronic newspaper and rated their relevance. Independently, the filtering method is applied to the same items and generated, for each participant, a list of news items ranked according to relevance.
Findings – The results of the evaluations revealed that the filtering algorithm, which takes into consideration hierarchically related concepts, yielded significantly better results than a filtering method that takes only common concepts into consideration. The paper determined a best set of values (weights) of the hierarchical similarity parameters. It also found out that the quality of filtering improves as the number of items used for implicit updates of the profile increases, and that even with implicitly updated profiles, it is better to start with user-defined profiles.
Originality/value – The proposed content-based filtering method can be used for filtering not only news items but items from any domain, and not only with a three-level hierarchical ontology but any-level ontology, in any language.

Traditional content-based filtering methods usually utilize text extraction and classification techniques for building user profiles as well as for representations of contents, i.e. item profiles. These methods have some disadvantages e.g. mismatch between user profile terms and item profile terms, leading to low performance. Some of the disadvantages can be overcome by incorporating a common ontology which enables representing both the users' and the items' profiles with concepts taken from the same vocabulary.
We propose a new content-based method for filtering and ranking the relevancy of items for users, which utilizes a hierarchical ontology. The method measures the similarity of the user's profile to the items' profiles, considering the existing of mutual concepts in the two profiles, as well as the existence of "related" concepts, according to their position in the ontology. The proposed filtering algorithm computes the similarity between the users' profiles and the items' profiles, and rank-orders the relevant items according to their relevancy to each user. The method is being implemented in ePaper, a personalized electronic newspaper project, utilizing a hierarchical ontology designed specifically for classification of News items. It can, however, be utilized in other domains and extended to other ontologies.