Related papers: On Content-Based Recommendation and User Privacy i…
This work addresses the challenges related to attacks on collaborative tagging systems, which often comes in a form of malicious annotations or profile injection attacks. In particular, we study various countermeasures against two types of…
The growing reliance on online services underscores the crucial role of recommendation systems, especially on social media platforms seeking increased user engagement. This study investigates how recommendation systems influence the impact…
Our online lives generate a wealth of behavioral records -'digital footprints'- which are stored and leveraged by technology platforms. This data can be used to create value for users by personalizing services. At the same time, however, it…
This paper explores recommender systems in social networks which leverage information such as item rating, intra-item similarities, and trust graph. We demonstrate that item-rating information is more influential than other information…
Social network websites, such as Facebook, YouTube, Lastfm etc, have become a popular platform for users to connect with each other and share content or opinions. They provide rich information for us to study the influence of user's social…
Recommender systems are an integral part of online platforms that recommend new content to users with similar interests. However, they demand a considerable amount of user activity data where, if the data is not adequately protected,…
The rise in popularity of microblogging services like Twitter has led to increased use of content annotation strategies like the hashtag. Hashtags provide users with a tagging mechanism to help organize, group, and create visibility for…
Privacy and Fairness both are very important nowadays. For most of the cases in the online service providing system, users have to share their personal information with the organizations. In return, the clients not only demand a high…
In collaborative recommendation systems, privacy may be compromised, as users' opinions are used to generate recommendations for others. In this paper, we consider an online collaborative recommendation system, and we measure users' privacy…
The social media site Flickr allows users to upload their photos, annotate them with tags, submit them to groups, and also to form social networks by adding other users as contacts. Flickr offers multiple ways of browsing or searching it.…
In this paper, based on the user-tag-object tripartite graphs, we propose a recommendation algorithm, which considers social tags as an important role for information retrieval. Besides its low cost of computational time, the experiment…
Recommender system is an essential component of web services to engage users. Popular recommender systems model user preferences and item properties using a large amount of crowdsourced user-item interaction data, e.g., rating scores; then…
Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than…
Recommender systems have become a pervasive part of our daily online experience, and are one of the most widely used applications of artificial intelligence and machine learning. Therefore, regulations and requirements for trustworthy…
A distributed classification paradigm known as collaborative tagging has been widely adopted in new Web applications designed to manage and share online resources. Users of these applications organize resources (Web pages, digital…
In this paper, we propose a novel tag-based recommender system called PLIERS, which relies on the assumption that users are mainly interested in items and tags with similar popularity to those they already own. PLIERS is aimed at reaching a…
The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. This data provides opportunities for…
Recommendation systems have received considerable attention in the recent decades. Yet with the development of information technology and social media, the risk in revealing private data to service providers has been a growing concern to…
Tag Management Systems were developed in order to support website publishers in installing multiple third-party JavaScript scripts (Tags) on their websites. Google developed its own TMS called ``Google Tag Manager'' (GTM) that is currently…
In recent years, with the enormous explosion of web based learning resources, personalization has become a critical factor for the success of services that wish to leverage the power of Web 2.0. However, the relevance, significance and…