Recommender systems are mostly well known for their applications in e-commerce sites and are mostly static models. Classical personalized recommender algorithm includes item-based collaborative filtering method applied in Amazon, matrix factorization based collaborative filtering algorithm from Netflix, etc. In this article, we hope to combine traditional model with behaviour pattern extraction...

One of the more rapidly growing areas in search technology is image search. With this availability comes the natural need to filter offensive content, to prevent Pornography images from reaching the wrong eyes. Filtering and blocking software is one of the most frequently touted prevention devices. As any user of these services is aware, they often fail to remove offensive images. The reasons a...

Sentences extracted from Twitter have been seen as a valuable resource for response generation in dialogue systems. However, selecting appropriate ones is difficult due to their noise. This paper proposes tackling such noise by syntactic filtering and content-based retrieval. Syntactic filtering ascertains the valid sentence structure as system utterances, and content-based retrieval ascertains...

Focusing on the uncertainty of classifying emails based-on email content and the incompleteness of email representation, the paper proposes a new representation using noncharacteristic information. The new approach refers to the whole email, contains feature items extracted from email content, and noncharacteristic items extracted from email header. In the expriment, we adopt Naïve Bayes classi...

According to Daily increase of the documents on the internet, automatic language detection is getting more important. In this paper we used language detection system to classify and filtering of the immoral web pages, based on their contents. This system could detect 10 most used languages in the immoral web pages, including FARSI language. As a technique we introduce a new combined method whic...

In consideration to the today’s globalized world everybody in the society are being addicted in using the Social

In this paper we compare the use of several features in the task of content filtering for video social networks, a very challenging task, not only because the unwanted content is related to very high-level semantic concepts (e.g., pornography, violence, etc.) but also because videos from social networks are extremely assorted, limiting the use of a priori information. We propose a simple method...

Collaborative Filtering and Content-Based Filtering are techniques used in the design of Recommender Systems that support personalization. Information that is available about the user, along with information about the collection of users on the system, can be processed in a number of ways in order to extract useful recommendations. There have been several algorithms developed, some of which we ...

So fast, so cheap, so efficient, Internet is nowadays incontestably communication mean of choice for personal, business and academic purposes. Unfortunately, Internet has not only this beautiful face. Malicious activities enjoy as well this so fast, cheap and efficient mean. The last decade, Internet worms took the lights. In the recent years, spams are invading one of the most used services of...

Due to the huge amount of information available online, the need of personalization and filtering systems is growing permanently. Recommendation systems constitute a specific type of information filtering technique that attempt to present items according to the interest expressed by a user. Commonly online recommender are employed for e-commerce applications or customer adapted websites. In gen...