Using Rich Social Media Information for Music Recommendation via Hypergraph Model

Abstract

There are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two main challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. We propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations.

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

“…They are useful for forecasting users' inclinations, because the users' interests may be governed by their friends or neighbors in interest groups. A lot of work has already been undertaken for utilizing friendship relations for recommendation [9]. The social filtering of links in social network to discover user's trust network constitutes the inherent implicit data of user.…”

“…and social tagging systems (STS) provides powerful way for users to organize, administer, consolidate and search for innumerable kinds of resources. These tags [8], [9] , [12] , [15] , [17], [18] carry interesting information about the preference of users who make the tags and of course about the labelled items itself. For example, Last.fm allows users showcase their preferences by tagging artists, albums or music tracks and Del.icio.us allows users to tag webpages.…”

“…They also compared the MRH algorithm with MRH-variant algorithms and some traditional methods. They found that the proposed algorithm significantly outperforms its variants and traditional recommendation algorithms [9]. Jian Jin and Qun Chen (2012) proposed a Top-K recommender system which is based on social tagging network.…”

“…This led to the second phase of the evolution in recommendation system. With the rapid expansion of Web 2.0, these systems incorporated social information [8] [9], [10], [11], [12] along with information used in traditional recommendation system, leading to the development of Social based Recommendation systems [13], [14], [15], [2] [16], [17] [18]. This social information was related to the virtual social circle of the user.…”

Incorporating group recommendations to recommender systems: Alternatives and performance

“…The experiments will be carried out using MovieLens and Netflix datasets. papers are focused on movie recommendation studies (Winoto & Tang, 2010;CarrerNeto, Hernández-Alcaraz, Valencia-García, & García-Sánchez, 2012); however, a great volume of literature for RS is centered on different topics, such as music (Lee, Cho, & Kim, 2010;Nanolopoulos, Rafailidis, Symeonidis, & Manolopoulos, 2010;Tan, Bu, Chen, & He, 2011), television (Yo, Zhou, Hao, & Gu, 2006;Barragáns-Martínez, Costa-Montenegro, Burguillo, Rey-López, Mikic-Fonte, & Peleteiro, 2010), books (Goldberg, Roeder, Gupta, & Perkins, 2001; Núñez-Valdés, Cueva-Lovelle, Sanjuán-Martínez, Ordoñez de Pablos, & Monegro Marín, 2012), documents (Porcel, Moreno, & Herrera-Viedma, 2009;Porcel & Herrera-Viedma, 2010;Serrano-Guerrero, HerreraViedma, Olivas, Cerezo, & Romero, 2011;Porcel, Tejada-Lorente, Martínez, & Herrera-Viedma, 2012), e-learning (Zaiane, 2002;Bobadilla, Serradilla, & Hernando, 2009), e-commerce (Huang, Zeng, & Chen, 2007;Castro-Schez, Miguel, Vallejo, & López-López, 2011), applications in markets (Costa-Montenegro, Barragáns-Martínez, & Rey-López, 2012), tourism (Borràs, Moreno, & Valls, 2014) and web search (McNally, O'mahony, Coyle, Briggs, & Smyth, 2011;Zhang, Yu, Fang, You, Liu, Liu, & Yan, 2014), among others.…”