OMUS: an optimized multimedia service for the home environment

Dooms, Simon; Pessemier, Toon De; Verslype, Dieter; Nelis, Jelle; Meulenaere, Jonas De; Broeck, Wendy Van den; Martens, Luc; Develder, Chris

doi:10.1007/s11042-012-1347-y

Cited by 7 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be used to measure popularity of products, person or things. This can also be seen in social web, such as Facebook that most number of users has about 130 friends, while very few users have larger social networks [18]. However, some persons do have more than 6,000 friends.…”

Section: Zipf's Lawmentioning

confidence: 82%

Social Popularity based SVD++ Recommender System

Kumar¹,

Verma²,

Rastogi³

2014

IJCA

View full text Add to dashboard Cite

Recommender systems have shown a lot of awareness in the past decade. Due to their great business value, recommender systems have also been successfully deployed in business, such as product recommendation at flipkart, HomeShop18, and music recommendation at Last.fm, Pandora, and movie recommendation at Flixstreet, MovieLens, and Jinni. In the past few years, the incredible growth of Web 2.0 web sites and applications constitute new challenges for Traditional recommender systems. Traditional recommender systems always ignore social interaction among users. But in our real life, when we are asking our friends or looking opinions, reviews for recommendations of Mobile or heart touching music, movies, electronic gadgets, restaurant, book, games, software Apps, we are actually using social information for recommendations. In this paper social popularity factor are incorporated in SVD++ factorization method as implicit feedback to improve accuracy and scalability of recommendations.

show abstract

Section: Zipf's Lawmentioning

confidence: 82%

Social Popularity based SVD++ Recommender System

Kumar¹,

Verma²,

Rastogi³

2014

IJCA

View full text Add to dashboard Cite

show abstract

“…There are only three papers on this theme. For example, Figure 19 shows how Dooms et al and DePessemier et al offered this functionality in their system called Omus [47,53]. Additionally, Recio-Garcia and Jimenez-Diaz proposed to explain group recommendations using AR, allowing all users to invite members to their groups [164].…”

Section: The Interactive Approaches That Address Group Formation and ...mentioning

confidence: 99%

A Systematic Review of Interaction Design Strategies for Group Recommendation Systems

Alvarado

Htun

Jin

et al. 2022

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

Systems involving artificial intelligence (AI) are protagonists in many everyday activities. Moreover, designers are increasingly implementing these systems for groups of users in various social and cooperative domains. Unfortunately, research on personalized recommendation systems often reports negative experiences due to a lack of diversity, control, or transparency. Providing a meta-analysis of the interaction design strategies for group recommendation systems (GRS) offers designers and practitioners a departure to address these issues and imagine new interaction possibilities for this context. Therefore, we systematically reviewed the ACM, IEEE, and Scopus digital libraries to identify GRS interface designs, resulting in a final corpus of 142 academic papers. After a systematic coding process, we used descriptive statistics and thematic analysis to uncover the current state of the art regarding interaction design strategies for GRS in six areas: (1) application domains; (2) devices chosen to implement the systems; (3) prototype fidelity; (4) strategies for profile transparency, justification, control, and diversity; (5) strategies for group formation and final group consensus; and, (6) evaluation methods applied in user studies during the design process. Based on our findings, we present an exhaustive typology of interaction design strategies for GRS and a set of research opportunities to foster human-centered interfaces for personalized recommendations in cooperative and social computing contexts.

show abstract

“…Both approaches have its own downsides; CF requires user data making it unsuitable for new items, while CB could produce only obvious recommendations. CF-CB hybrids could potentially overcome these limitations [DDV14].…”

Section: Related Workmentioning

confidence: 99%

Finding Similar Movies: Dataset, Tools, and Methods

Leng¹,

Paulino²,

Haider³

et al. 2018

CSRN

View full text Add to dashboard Cite

Recommender systems are becoming ubiquitous in online commerce as well as in video-on-demand (VOD) and music streaming services. A popular form of giving recommendations is to base them on a currently selected product (or items), and provide "More Like This," "Items Similar to This," or "People Who Bought This also Bought" functionality. These recommendations are based on similarity computations, also known as item-item similarity computations. Such computations are typically implemented by heuristic algorithms, which may not match the perceived item-item similarity of users. In contrast, we study in this paper a data-driven approach to similarity for movies using labels crowdsourced from a previous work. Specifically, we develop four similarity methods and investigate how user-contributed labels can be used to improve similarity computations to better match user perceptions in movie recommendations. These four methods were tested against the best known method with a user experiment (n = 114) using the MovieLens 20M dataset. Our experiment showed that all our supervised methods beat the unsupervised benchmark and the differences were both statistically and practically significant. This paper's main contributions include user evaluation of similarity methods for movies, user-contributed labels indicating movie similarities, and code for the annotation tool which can be found at http://MovieSim.org.

show abstract

OMUS: an optimized multimedia service for the home environment

Cited by 7 publications

References 19 publications

Social Popularity based SVD++ Recommender System

Social Popularity based SVD++ Recommender System

A Systematic Review of Interaction Design Strategies for Group Recommendation Systems

Finding Similar Movies: Dataset, Tools, and Methods

Contact Info

Product

Resources

About