Trait Analysis to Facilitate Children’s Books Recommender Systems: Extracting perspective and sentiment traits from book fulltext and descriptions

Recommender systems are a useful tool for match- ing readers with books. However, the lack of user data from children, both due to privacy concerns as well as a low incentive to leave reviews, results in existing systems proving inadequate at recom- mending to the youth. It has been shown that chil- dren have a different relation regarding...

Metadata, the undiscovered treasure box for book recommender systems for children: What traits can be inferred from book’s descriptive and structural metadata which could support the designers of books recommender systems for children?

This study examines traits that may be derived from book metadata and identifies statistically signifi- cant patterns and trends in order to assist recom- mender system designers for children’s books in selecting the most important traits to take into ac- count. This research focuses on descriptive such as publish year and structural metadata...

Covering Covers: Characterization Of Visual Elements Regarding Sleeves

The cover of a children's book could play a crucial role in attracting the attention of young readers and influencing their decision to pick up and read the book. In this study, we aim to identify the factors that contribute to the appeal of children's book covers. Given that the target audience for children's books changes as children grow...

Evaluating Design Choices in Tripartite Graph-Based Recommender Systems to Improve Long Tail Recommendations

Even though the abaility to recommend items in the long tail is one of the main strengths of recommendation systems, modern models still show decreased performance when recommending these niche items. Various bipartite and tripartite graph-based models have been proposed that are specifically tailored to solving this long tail issue. This study...

The Performance of Total Variation Regularizer for User Collaborative Filtering

Recommender systems (RS) assist users in making decisions by filtering content that the user would likely find relevant. Standard techniques like collaborative filtering exploit user similarities to find the recommendations assuming that similar users are likely to be interested in the same items. On the other hand, graph RS borrow techniques...

The Platform, the Creator, and the Viewer: An analysis of values and emotions on the impacts of parasocial phenomena on YouTube

When discussing media, parasocial phenomena is mentioned. Audience members naturally partake in parasocial phenomena when consuming media and empathizing with media figures. Viewers who form relationships with a media figure despite never meeting them are said to be in a parasocial relationship. On YouTube, examples of parasocial interactions...

From Talents to Team: Matching supply and demand with algorithms

The globalizing economy with its new goods and services, knowledge spread, and competition for talent is an increasing complexity for organizations, which requires organizations to adapt more quickly. Organizations are essential to society, as people are more productive in groups. For their continuity, it is important that organizations...

Context-Aware Recommender Systems in the E-commerce Domain

Recommender systems help users explore a large data set by proposing items in that data set that the system expects to be of interest to that user. The use of context, information that describes in which situation a user interacts with the recommender system, has shown to increase the effectiveness of recommender systems in several domains. In...

Bursting Filter Bubbles With Serendipity

When talking about personalization online, Google CEO Eric Schmidt recently said "it will be very hard for people to watch or consume something that has not in some sense been tailored for them." This level of personalized filtering of content has worried academics and activists. Many argue that users will be trapped in a so-called "Filter...

Temporal Preference Analysis in Recommender Systems

The thesis presents the results of research into temporal preference analysis in recommender systems. Temporal preference analysis consists of methods for detecting time recurrent changes in user preferences and for using this information to improve the recommendation precision.